EXPLORING ONLINE IDENTITIES OF INFLUENTIAL

USERS IN ONLINE COMMUNITIES: A MIXED

METHODS APPROACH

A thesis submitted in fulfilment of the requirements for admission to the degree of

Doctor of Philosophy

University of the West of England

Samantha Richardson

July

2021

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“It always seems impossible until it’s done.”

―Nelson Mandela

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ABSTRACT

At present the use of online communities has become a great interest to

practitioners and scholars alike due to the potential for recruitment and social marketing

(Johnson, Safadi, & Faraq, 2015). However, despite the growth in interest for online

communities and the vast potential that online communities offer, fairly little is known

about who influences these groups and the social dynamics of influence. This research

aimed to advance existing knowledge on online communities and examine the online

identities and behaviours of individuals who gain momentum and status within online

communities and how they subsequently lose this perceived status amongst followers.

The present research utilised a pragmatic, mixed methods approach to exploring

influential users. The first phase of the research employed quantitative methods to

determine role transition in two online communities. Using secondary data, the cluster-

analysis illustrated those who had been influential over a two-year period and the

various role transitions that occurred within that time frame. From this, the present

research was able to assess particular online behaviours associated with influential

individuals and identify those deemed influential. A MANOVA revealed that those

categorised as always influential over the two-year period had higher posts, threads,

word count for threads, number of thanks, and reputation and average number of thanks

in comparison with their counterparts. Additionally, social network analysis and a

number of fixed effects are discussed in relation to status. There were no significant

differences found between the two different online discussion sites.

Phase two of the research was conducted via narrative analysis of 16 online

community users (eight from LWP and eight from IU). A detailed examination of the

themes are discussed, the journey that individuals embarked upon when entering a new

forum and this then illustrates how individuals try to seek acceptance from others within

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their communities. This explored the online identities of influential individuals from a

Social Identity Theory perspective (Tajfel & Turner, 1979). Social identity is key in

understanding online community life and understanding how influential individuals

relate to their salient roles embedded in the community. Consequently, this research

contributes to knowledge by ulitising this theory to explore social identities of those

who gain and lose status and how their identities transpire through online

communication. The research has produced some key questions and interesting ideas

for future research with regards to undermining credibility in online forums, which

could be incorporated into government strategy for counter-terrorism interventions.

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This is to certify that

(i) the thesis comprises only my original work towards the PhD,

(ii) acknowledgement have been made to funders, supervisory team and the

University

(iii) my thesis is less than 100,000 words in length

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ACKNOWLEDGEMENTS

To everyone that has made this thesis a reality; I would like to give you a

massive thank you. All the individuals that regularly contributed to their own online

communities, these are your words and you have provided insight into a valuable topic.

I would like to thank my family; Mum, Dad and Amirah for being there continually and

pushing me to work harder and generally be a better person. This is for you.

I would like to thank my funders and of course supervisory team Dr Gareth

Edwards and Dr Adam Joinson, not to mention Dr Helen Frisby in the graduate school

for your continued help and support over the years I honestly never thought that I would

ever get this far. Your help and support has been hugely appreciated.

Finally, Andy Grant who spent hours teaching me how to use MySQL. You are

a lifesaver!

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LIST OF TABLES

Table 3.1 Characteristics of the Basic Paradigms of Social Research

Table 3.2 Research Questions/Hypotheses for Research Outline

Table 3.3 Table Showing Groups Use in Phase Two from LWP and IU

Table 3.4 Table Showing the Four Different Approaches to Narrative

Analysis

Table 4. 1

Table 4.2

Table Showing Role Transitions for Eight Users LWP

Hypothesis for Phase One

Table 4.3 Group Properties for the Independent Variable Status

Table 4.4

Group Status Mean and Standard Deviation Scores for

Dependent Variable Reputation

Table 4.5

Group Status Mean and Standard Deviation Scores for

Dependent Variable Post Total and Post Word Count

Table 4.6

Group Status Mean and Standard Deviation Scores for

Dependent Variables Thread Total and Thread Word Count

Table 4.7

Group Status Mean and Standard Deviation Scores for

Dependent Variables Average Thread Word Count and Average

Post Word Count

Table 4.8

Group Status Mean and Standard Deviation Scores for

Dependent Variable Number of Thanks Received and Average

Number of Thanks

Table 4.9 Descriptive Statistics for Status Group Monthly Thread

Frequency

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Table 4.10

Table 4.11

Descriptive Statistics for Status Group Monthly Post Frequency

Descriptive Statistics for Status Group and Number of Thanks

Frequency

Table 4.12 Descriptive Statistics for Status Group and Average Number of

Thanks Frequency

Table 4.13 Role transitions for eight IU members over a 24 month period.

Table 4.14 Group Properties for the Independent Variable Status

Table 4.15 Group Status Mean and Standard Deviation Scores for Dependent

Variable

Table 4.16 Group Status Mean and Standard Deviation Scores for

Dependent Variables Post Total and Post Word Count

Table 4.17

Group Status Mean and Standard Deviation Scores for

Dependent Variables Thread Total and Thread Word Count

Table 4.18

Group Status Mean and Standard Deviation Scores for

Dependent Variables Average Thread Word Count and Average

Post Word Count

Table 4.19

Group Status Mean and Standard Deviation Scores for

Dependent Variable Number of Thanks Received and Average

Number of Thanks

Table 4.20

Descriptive Statistics for Status Group Monthly Thread

Frequency

Table 4.21

Descriptive Statistics for Status Group Monthly Thread

Frequency

Table 4.22

Descriptive Statistics for Group Status and Monthly Number of

Thanks

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Table 4.23

Descriptive Statistics for Group Status and Monthly Average

Number of Thanks

Table 4.24

Betweenness Centrality Descriptive Statistics for Influential

Individuals of IU and LWP.

Table 4.25

Table 4.26

PageRank Descriptive Statistics for Influential Members of IU and

LWP.

Hypotheses Summary Table

Table 6.1

Table Displaying Similarities and Differences in Behaviour

Between Members of LWP and IU

Table 6.2 Table Illustrating Key Themes and Subthemes for Influential

Individuals that Gained Status

Table 6.3 Table Illustrating Key Themes and Subthemes for Influential

Individuals that Lost Status

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LIST OF FIGURES

Figure 2.1 Diagram of “Two-Step Flow Model of Influence”

Figure 3.1 Screenshot of User Information and Reputation Scores

Figure 3.2 Diagram of Threaded Discussion

Figure 3.3 Diagram of Reader-to-Leader Framework

Figure 3.4 Query Used For MySQL Database

Figure 3.5 Query Used to Create Table for MySQL Database

Figure 3.6 Diagram showing the various nodes and edges in a social network

analysis.

Figure 4.1 Line Graph Showing Mean Threads Each Month for the Status

Groups

Figure 4.2 Line Graph Showing the Mean Number of Posts Each Month for

the Status Groups

Figure 4.3 Line Graph Showing the Mean Number of Thanks Each Month for

Status Groups

Figure 4.4 Line Graph Showing Average Number of Thanks Each Month for

Status Groups

Figure 4.5 Line Graph Showing Mean Number of Threads Started Each

Month for Status Group IU

Figure 4.6 Line Graph Showing Mean Number of Posts Started Each Month

for Status Groups IU

Figure 4.7 Line Graph Showing Mean Number of Thanks Each Month for

Status Groups IU

Figure 4.8 Line Graph Showing Mean Average Number of Thanks Each

Month for Status Groups IU

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Figure 5.1 Themes and Subthemes of Part One for LWP.

Figure 5.2 Screenshot of Polls Started in Community Discussion by Guerrilla

Warfare

Figure 5.3 Threads Moved to Trashcan as Too Controversial

Figure 5.4 Themes and Subthemes for Part One LWP

Figure 5.5 Themes and Subthemes for Part One IU

Figure 6.1 Themes and subthemes of Part Two for LWP.

Figure 6.2 Screenshot of Posting Behaviour for community member Kay

Figure 6.3 Screenshot of Repetitive Behaviour for community member

Brothering

Figure 6.4 Themes and Subthemes for Part Two IU

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TABLE OF CONTENTS

CHAPTER ONE .......................................................................................................... 17

BACKGROUND AND OVERVIEW ......................................................................... 17 1.1. Background ....................................................................................................... 17

1.2. Introduction ....................................................................................................... 17

1.3. Theoretical Framework ..................................................................................... 20

1.4. Philosophical Stance ......................................................................................... 22

1.5. Narrative Analysis ............................................................................................ 23

1.6. Structure of Thesis ............................................................................................ 23

1.7. Contribution to Knowledge ............................................................................... 25

CHAPTER TWO ......................................................................................................... 27

LITERATURE REVIEW ............................................................................................ 27 2.1. Online Communities ......................................................................................... 27

2.1.1. User Participation in Online Communities ................................................ 28

2.1.2. Credibility in Online Communities ............................................................ 34

2.2. Communicator Characteristics .......................................................................... 37

2.3. Language and Authority ................................................................................... 46

2.4. Rationale for Present Research ......................................................................... 49

2.5 Theoretical Framework: Social Identity Theory ................................................ 49

2.5.1. Social Identity ............................................................................................ 49

2.5.2. Social Identity and the Internet .................................................................. 51

2.5.3. Group Processes ......................................................................................... 52

2.5.4. Limitations of Social Identity Theory ........................................................ 54

2.5.5. Contribution to Theoretical Framework .................................................... 54

2.6. Research Questions and Aims .......................................................................... 55

2.7. Contribution to Knowledge ............................................................................... 55

CHAPTER THREE ..................................................................................................... 57

METHODOLOGY ...................................................................................................... 57

3.1. Chapter Overview ............................................................................................. 57

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3.2. Paradigms of Social Research ........................................................................... 57

3.2.1. Ontology .................................................................................................... 58

3.2.2. Epistemology ............................................................................................. 59

3.3. Present Research Design ................................................................................... 59

3.4. Research Outline ............................................................................................... 61

3.5 Community Selection ......................................................................................... 63

3.6 User Selection .................................................................................................... 66

3.7 Data Collection for Study One ........................................................................... 69

3.7.1. Sampling .................................................................................................... 70

3.7.2. Selection Criteria ....................................................................................... 71

3.7.3. Analysis ..................................................................................................... 72

3.8 Data Collection for Study Two .......................................................................... 74

3.8.1.Design ......................................................................................................... 74

3.8.2. Sample ....................................................................................................... 75

3.8.3. Analysis ..................................................................................................... 80

3.8.4.Triangulation ............................................................................................... 84

3.9 Ethical Considerations ....................................................................................... 85

CHAPTER FOUR ........................................................................................................ 87

STUDY ONE: QUANTITATIVE ANALYSIS OF ROLE TRANSITIONS IN

ONLINE COMMUNITY............................................................................................. 87 4.1. Overview of Chapter ......................................................................................... 87

4.3. Left Wing Politics Analysis .............................................................................. 87

4.4 Summary of LWP members and Hypothesis Development .............................. 88

4. 2. Summary of Hypotheses: ................................................................................. 89

4.5. SNA for Online Community Members ............................................................. 90

4.6 Global LWP Descriptive and Inferential Statistics ............................................ 91

4.6.1. Inferential Statistics ................................................................................... 94

4.7. Mixed Effects Models ....................................................................................... 99

4.7.1 Thread Frequency and Status Group........................................................... 99

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4.7.2. Post Frequency and Status Group ............................................................ 101

4.7.3. Number of Thanks Frequency and Status Group ..................................... 103

4.7.4. Average Number of Thanks Frequency and Status Group ...................... 105

4.8 Islamic United Analysis ................................................................................... 109

4.8.3. Descriptive Statistics ................................................................................ 111

4.8.4. Inferential Statistics ................................................................................. 113

4.8.5. Mixed Effects Models .............................................................................. 117

4.9. Comparing Influence between Communities .................................................. 127

4.9.1. Method of Analysis .................................................................................. 128

4.10. Chapter Summary ......................................................................................... 129

CHAPTER FIVE: ...................................................................................................... 135

STUDY TWO: NARRATIVE ANALYSIS OF INFLUENTIALS IN ONLINE

COMMUNITIES ......................................................... Error! Bookmark not defined. 5.1. Overview and Summary of Research Questions/Aims .... Error! Bookmark not

defined.

5.2. The Rise to Influence Analysis LWP ................ Error! Bookmark not defined.

5.2.1.Introduction to Left-Wing Politics .............. Error! Bookmark not defined.

5.3. Left-Wing Politics Analysis .............................. Error! Bookmark not defined.

5.3.1. Part One: Becoming Influential ................. Error! Bookmark not defined.

5.3.2. Theme Two: Adopting a Role ................... Error! Bookmark not defined.

5.3.3. Theme Three: Maintaining Credibility ...... Error! Bookmark not defined.

5.3.4. Summary of Part One: How Influential Individuals Become Reputatable.

............................................................................. Error! Bookmark not defined.

5.4. The Rise to Influence Analysis Islamic United Error! Bookmark not defined.

5.4.1. Theme One: Integration ................................. Error! Bookmark not defined.

5.4.2. Theme Two: Role Formation ..................... Error! Bookmark not defined.

CHAPTER SIX: ......................................................................................................... 136

LOSING STATUS ....................................................... Error! Bookmark not defined. 6.1. Overview of Chapter ......................................... Error! Bookmark not defined.

6.2 Losing Status LWP Analysis ............................. Error! Bookmark not defined.

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6.2.1. Theme One: Retrogression ........................ Error! Bookmark not defined.

6.2.2. Theme Two: Disengagement ..................... Error! Bookmark not defined.

6.3. Analysis of Losing Status for Islamic United ... Error! Bookmark not defined.

6.4. Theme One: Disengagement ............................. Error! Bookmark not defined.

6.4.1 Subtheme one: The dissolution of self-identity ......... Error! Bookmark not

defined.

6.4.2. Subtheme Two: The dissolution of social-identity ... Error! Bookmark not

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6.5.3. Theme One Summary ................................ Error! Bookmark not defined.

6.6. Chapter Summary ............................................. Error! Bookmark not defined.

6.7 Discussion of Qualitative Data .......................... Error! Bookmark not defined.

CHAPTER SEVEN ................................................................................................... 136

GENERAL DISCUSSION ........................................................................................ 137

CHAPTER EIGHT .................................................................................................... 145

LIMITATIONS, CONCLUSIONS, FURTHER RESEARCH AND RELFECTIONS

.................................................................................................................................... 145 8.1 Limitations ....................................................................................................... 145

8.2. Future Directions and Implications ................................................................. 149

8.3. Conclusions and Knowledge Contributions .................................................... 151

8.4. Summary of Key Limitations .......................................................................... 154

8.5. Summary of Practical Implications ................................................................ 155

8.6. Summary of Future Recommendations for Research ..................................... 155

8.7. Summary of Gaining/Losing Influence in Online Communities .................... 156

CHAPTER NINE ....................................................................................................... 158

REFLECTION ON MY JOURNEY .......................................................................... 158 9.1. Literature Review ............................................................................................ 160

9.2. Methodology ................................................................................................... 162

9.3.Study One - Quantitative Examination of Role Transitions ............................ 166

9.4. Study Two – Narrative Analysis of Influential Individuals ............................ 168

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9.5. Discussion ....................................................................................................... 169

References .................................................................................................................. 172 APPENDICES ....................................................................................................... 205

Appendix 1: Ethical Approval ............................................................................... 205

Appendix 2: MySQL Queries ................................................................................ 206

Appendix 3: Sample of SPSS Output for Phase One ............................................. 209

Appendix 4: Tables from Quantitative Chapter ..................................................... 242

Appendix 5: Sample Theme Table for Phase Two ................................................ 296

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CHAPTER ONE

BACKGROUND AND OVERVIEW

1.1. Background

From a personal view, I have always believed that individuals interact

differently online due to the anonymity. Having come from a social psychological

background, online interactions have always been the focus of my research and sparked

my interest in the area of cyberpsychology and behaviour. As such, when I initially

embarked on my journey it was a funded PhD to examine online groups and

interactions. However, over time the focus of the PhD became narrower and attention

to influentials and credibility became the focal point of the research.

One of the main difficulties I encountered through this thesis is the notion of

leadership and influentials. Indeed, when the focus of the research narrowed to popular

individuals it brought forth questions about leaders and leadership. Having

permanently struggled to comprehend the notion of leadership in online communities,

it was removed from the thesis as the research began to focus on those individuals who

had influence but did not necessarily hold a position of power. For example, Kim

Kardashian is not a CEO or World leader, but has a level of interpersonal influence. It

is this level of influence that the thesis will examine in-depth as it is rooted within my

research interests and background. Whilst there are instances where individuals are

referred to as ‘leaders’, this terminology has only been utilised if the research authors

themselves in previously literature have used that term and it is relevant to the study of

influentials in online communities.

1.2. Introduction

Online communities have been described as social-relationship aggregation,

which is facilitated by internet-based technological advancements (Lin & Lee, 2006).

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These communities are social hubs where individuals can communicate and share

resources with one another (Preece, 2001). These large groups of internet users reflect

a considerable proportion of global internet use as they manifest in many forms; such

as social networking sites, discussion forums, blogs or message boards (Horrigan,

2001; Huffaker, 2010). For instance, in 2009, Facebook (founded in 2004) had a

staggering 175 million active community members (Jameson, 2009). Since then,

however, Facebook now has over 2.8 billion monthly users (McCarthy, 2020,

Protalinski, 2012; Statista, 2018). Likewise, YouTube have an average of 10 billion

video views (per month) making it the third most active website in the world, after

Facebook and Google (Preece & Shneiderman, 2009).

Evidently, these online forums are expanding at a rapid pace and are now

becoming integrated and common-place in everyday life. Additionally, due to the

recent COVID-19 pandemic, remote working and online communities have dispersed

into the workplace (Bavel et al., 2020; Baym, 2000; Rheingold, 2000) and educational

settings (Martin, 2021; Pittinsky, 1999; 2003; Sherer, Shea, & Kritensen, 2003). The

past year has seen 1.32 billion students adapt to remote e-learning across the world

(McCarthy, 2020). Moreover, the effects of numerous lockdowns and social isolation

have seen more people than ever take to digital platforms for emotional support and

social contact (Bavel et al., 2020; Duan & Zhu, 2020; Elmer, Mepham, & Stadtfeld,

2020; Fried, Papanikolaou, & Epskamp, 2020). Indeed, there is an online community

to support every interest or creative endeavor (Johnson, Safadi, & Faraj, 2015).

Consequently, understanding the social dynamics of behaviour in these communities

would provide invaluable insight for social sciences, marketing and communication

researchers alike. This knowledge on influencers could provide businesses and

companies techniques to strengthen their relationship with brands and consumers

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through digital advertising (Berne-manero & Marzo-Navarro, 2020; Khodabandeh &

Lindh, 2020).

Nevertheless, despite this recent growth in online communities very little is

known about who influences these groups and what the social structure of these

communities looks like given the lack of hierarchy online (Johnson et al., 2015).

Accordingly, in-depth understanding of the communication characteristics and social

network ties would enlighten research on influence and information propagation online.

Thus far, very little research has examined what makes an individual influential, or the

online identities of these influential users within online fora.

That is, what are the online behaviours and characteristics that make individuals

influential to onlookers? How can we identify influentials in online communities?

Therefore, the goal of the present research is to identify the roles of influential

individuals in online communities. Consequently, identifying what makes an individual

influential online can address a far more pressing issue, why do individuals lose their

influence? This question is one that is far less understood and researched in the

available literature but an in-depth exploration of these online identities does have

strong implications for those within the managerial and marketing setting as well as

possible implications for government policies and those working with internet security.

For example, this research can provide clarity to why some brands fail when others

flourish and also help develop strategies for government policies on counter-terrorism

with regards to online recruitment and influence.

Equally, the growing industry of “influencers” has become the focus of recent

research due to the leverage influential individuals have with respect to potential buyers

(Berne-manero & Marzo-Navarro, 2020; Khodabandeh & Lindh, 2020). As product

research and reviews are now readily available, information search is now an integrated

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part of the online shopping process (Flavián, Gurrea, & Orús, 2009). Therefore, brands

who incorporate social platforms into their marketing strategy and embrace influencers

to endorse their products would benefit from a greater understanding of how and why

influencing works in disseminating information to the masses. Therefore, this research

adds understanding to how influencers may lose their influence over time by exploring

these online identities.

At present the research relating to online identities perceives a ‘static conception

of identity’ through data driven techniques such as ‘personal information’ (Maden, Foz,

Smith, 2007; Zhang, Jiang, & Carroll, 2010). Thus, the current literature does not

examine the social interactions embedded in online community life. Moreover, the

dominant work on identities in cyberspace from Turkle (1999) focuses on game-play

rather than communities on multiplayer real-time virtual worlds (MUDs). Therefore,

the contribution from the present research is that it explores naturally occurring data in

online communities and examines such social networks and interactions sustained by

social identities for users that are particularly influential or popular (Zhang et al., 2010).

This examines how social identity and group categorisation is rooted within community

life and promoted in discussion. From this, the research will add to literature on online

identities and examine those that gain momentum within the community and

subsequently influentials that lose their status within the community to explore

interactions, roles and identities that alter over time.

1.3. Theoretical Framework

Having always had a preferential interest in the social psychology paradigm –

Social Identity Theory (SIT) has been a theoretical framework that I was quite familiar

with (Tajfel & Turner, 1979). Social Identity Theory was originally developed by

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Henri Tajfel (1974) and has become a key theory within social psychology as it is often

utilised to explain the influence of groups; group favouritism and prejudice.

John Turner and Henri Tajfel worked together and expanded this notion of

group influence to include Self-Categorisation Theory (Turner, 1975). Taken together,

these two influential theories are the foundations of the Social Identity Approach. This

framework examines how individuals self-identify as group members and how this

membership ultimately impact on their notion of identity and social categories. These

theories and current limitations are discussed further in chapter two.

Van de Mieroop (2015) notes that there has been previous shortcoming with the

literature on social identities as research posit “a priori ingroup-outgroup opposition”

(p. 410). Thus, existing research appears to be somewhat problematic and reductionist

in that it does not examine the shifting of identities (Coupland, 2010). Therefore, by

adopting this stance on group membership, previous research has omitted the social

construction of identity apparent in discourses and narratives. Equally, as identity is

primarily viewed as a product of social interaction, natural occurring data is far more

relevant to the social identity approach of online influentials than the existing literature

which assigns established categories (Widdecombe, 1998).

This particular approach has been the framework for the present investigation

as the way in which influential individuals develop their notion of identity and group

membership is central to their online identities and virtual selves (Turkle, 1999). Due

to the nature of anonymity, there are few social markers, therefore, their categorisation

and group identity within an online community is paramount to understanding how an

individual becomes influential within online communities, and subsequently why an

individual might lose their influence over others.

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1.4. Philosophical Stance

Equally, in choosing to utilise this theoretical framework, comes the issue of

epistemology and ontology (Creswell, 2003) which is the focus of chapter three of the

thesis. This thesis utilises a mixed methods approach to investigating influentials in

online communities. Investigating social identities requires an in-depth understanding

of the interactions of individuals (Van De Mieroop, 2015). Thus, this can be best

accomplished via narrative analysis as online identities and interactions are only

available via language and text, as the only source of communication. I had often

thought of myself as stemming from a constructivist ontology; interested in how

individuals perceive their social worlds and multiple realities (Creswell, 2003;

Reissman, 2008). However, I would now class myself as a pragmatist, as this journey

has thrown me into quantitative data and measurement which I would have previously

tried to avoid before this thesis.

I have maintained through the thesis that I was utilising a pragmatic approach

due to the mixed methodology; however, with mixed methods it is usually presumed

that qualitative research then leads onto the quantitative phase of research and that the

exploratory phase is essentially helping the researcher operationalise one’s hypotheses;

though, this was not the case with the final version of the methodology chapter three,

but pragmatic still appeared to be the most suitable approach as both parts were equally

as important. Therefore, chapter four (the first empirical phase) identifies influential

individuals and examines online behaviours congruent with that of previous literature

to determine who and when an online community member was particularly

popular/influential (Anger & Kittl, 2011; Bakshy, Hofman, Manson, & Watts, 2011;

Fisher, Smith, & Wesler, 2006; Joinson & Dove, n.d.; Ma & Agarwal, 1997).

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1.5. Narrative Analysis

Accordingly, after the influentials had been identified in the communities, the

next two chapters provided a narrative analysis of online influentials. Chapter five

examines individuals as they gain momentum within their online forums and chapter

six examines when they are losing influence. Van De Mieroop (2015) denotes that

narratives and narrative analysis is central in the exploration of social identities as the

way in which individuals discuss, converse and identify social categories is present

through language and social interactions. Utilising narrative analysis (with a prominent

thematic edge) 16 online community members that were identified from the

quantitative chapter as being influential, this was investigated further to examine online

behviours and interactions when they rise and lose status.

Whilst the narrative analysis does have a strong thematic edge, Reisman (2008)

claims this particular model of narrative analysis focuses more on the content and what

is being told by the individual, opposed to how it is told. Thus, this thematic narrative

analysis was deemed most suitable for the analysis to investigate online identities via a

social identity approach. Language is central to identity; therefore, the analysis focuses

on how individuals interact with on-an-other and how they embody their social

identities within the online forum.

1.6. Structure of Thesis

Chapter One Background and Overview

Chapter Two Literature Review

Chapter Three Methodology

Chapter Four Quantitative Analysis

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Chapter Five Narrative Analysis

(Gaining Influence)

Chapter Six Narrative Analysis

(Losing Influence)

Chapter Seven General Discussion

Chapter Eight Limitations, Conclusions, Future

Recommendations

Chapter Nine Reflection

While it does appear unusual to have two chapters focusing on narrative analysis

for a mixed methods approach. I had thought combining this into one global narrative

chapter would be far too long and this structure would lose coherence for the reader.

Moreover, by separating these chapters there is more of a narrative and story to the

journey of influentials which, in line with the theoretical framework would provide far

clearer insight into the fluidity of social identities. Therefore, chapter five examines

individuals when they are gaining momentum within their online communities and

chapter six examines online behaviours and interactions when they are losing their

status in their online communities.

Equally, I have also included a discussion of the qualitative findings in chapter

six – before the general discussion. I included this section because I felt that in some

ways the essence of the narrative analysis was lost with the contribution of mixed

methods to the area. As such, by having a discussion at the end of chapter six about

the findings provided a summary of the chapters and main findings in relation to the

theoretical framework ultimately created more coherence. Therefore, the general

discussion in chapter seven focused on the contribution to mixed methods in line with

the pragmatic stance taken for this thesis.

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This research is not without limitations and shortcoming, but rather than present

them here they are in chapter eight. Additionally, this chapter discusses some ideas for

future research and reiterates my knowledge contribution. Finally, I have put my

reflection in a new chapter (chapter nine). I believe this chapter is as important as the

other chapters as it documents my journey and the various issues I have overcome as a

researcher. I had initially included a lot of the information as an introduction, but I

believe that the reader must read the thesis, before reading my challenges.

1.7. Contribution to Knowledge

The aims of my research were:

• To identify influential individuals present in online communities

• To examine the online behaviours and characteristics that influential

individuals have that distinguishes them from their followers

• To examine the context of conversations online to establish the ways in which

influence can be gained and lost

• To explore online identities of influential users in online communities

This thesis contributes to the social identity approach by exploring online identities

on influential individuals. Specifically, by looking at how influentials embrace their

membership and interact with other members has enabled some guidance as to how and

why influentials gain and lose their status within online communities. Therefore, the

unique aspect of the research is that the extant literature on this topic is yet to focus on

diminishing influence of influential individuals, nor does the literature explore the

online journeys these influential community members embark on when gaining

momentum in an online community. This research adds knowledge to the question,

why do individual lose influence in their online communities? This question is the next

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logical progression for the research in this area, as this thesis illustrates that social

identities are not fixed in time.

Furthermore, this research advances understanding in this area by utilising a

qualitative narrative analysis approach to examine the transitions of influential

individuals and substantiate the existing literature available by providing a mixed

methods approach. Indeed, there are a variety of approaches which will be discussed

in the literature review but few have yet to qualitatively look at the rise and fall of

influential individuals in conjuncture with one-an-other.

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CHAPTER TWO

LITERATURE REVIEW

2.1. Online Communities

Online communities are virtual spaces “where people can come together with

others to converse, exchange information or other resources, learn, play or just be with

each-other” (Kraut & Resnick, 2011, p.1). Likewise, Preece (2001) describes these

social hubs as persistent collections of people with common interests whose primary

use of communication is the internet. These are rather broad terms that apply

themselves to many social configurations through the use of internet technology. Online

communities (or virtual communities as they are also known) have become very

popular internet applications due to their ability to attract and potentially sustain

membership (Code & Zaparynuik, 2007; Kraut & Resnick, 2011; Zhou, 2011), thus

thrusting these computerised subcultures into the limelight of social science and

information systems researchers alike.

A report issued by the International Telecommunications Union (ITU), in 2013,

stated that there was an estimated 2.3 billion internet users worldwide (ITU, 2013), this

figured nearly doubled in 2018 (Global Internet Statistics, 2018). Furthermore, internet

traffic is expected to grow and it is further predicted that more than half (53%) of the

global population will be surfing the net in 2020 (Germalto, 2019). According to a

recent report by the China Internet Network Information Centre (CNNIC), about half

of all these internet users have, at some point, accessed an online community (CNNIC,

2010). Facebook, the world’s largest online community, has 2.8 billion active monthly

users (McCarthy, 2020; Prigg, 2015). Equally, virtual worlds such as Second Life have

also seen an accelerated increase in users and activity (Korolov, 2011). Therefore, this

28

emerging arena has great potential for research as it is rapidly becoming commonplace

in everyday life.

2.1.1. User Participation in Online Communities

Paradoxically, while many scholars have discussed the growth of internet usage,

empirical research illustrates that there are fairly few online communities that gain

success and momentum (Ma & Agarwal, 2007; Zhou, 2011). For example, the majority

of communities on websites such as MSN (www.msn.com) have fewer than 25

members and sparse contributions amongst their online members (Ma & Agarwal,

2007). Bateman, Gray, and Butler (2011) articulate the pervasive nature of online

communities, claiming that a members’ contribution is entirely voluntary and the extent

to which one contributes is dependent upon free choice. Accordingly then, this brings

forth the question, if all online communities have these same equal opportunities why

do some succeed when others fail? In response to this notion, a vast amount of literature

has devoted time to investigating member commitment in a bid to determine what

makes successful online communities (Bateman et al., 2011; Zhou, 2011).

Perhaps it is not surprising that an emerging research agenda has dedicated

resources to investigating the intrinsic and extrinsic motivations of member

participation in these communities in a bid to understand successful communities and

sustained membership (Kankanhalli, Tan, & Kwok-Kee, 2005; Lakhani & von Hippel,

2003; Martinez-Torres & Diaz-Fernandez, 2014). Online community membership has

been found to be characterised by levels of engagement (Ren et al., 2012). Therefore,

the strength of an individual’s identification with a group and the interpersonal

relationships and bonds that are formed within these social hubs can determine their

level of participation. As such, online communities are characterised by expertise-

29

based authority, emergent roles, high turnover, interaction and participation (Johnson,

Safardi, & Faraj, 2015; Ren et al., 2007).

Accordingly, extant literature have applied many theories to the success of

online communities, the technology acceptance model (TAM), trust theory, social

capital theory and social network theory just to name a few, have all been utilised in an

attempt to explain online community participation amongst users (Ridings, Gefen,

Arinze, 2002; Hsu & Lu, 2007; Hsu, Ju, Yen, & Chang, 2007). TAM claims that

perceived usefulness (PU) and perceived ease of use (PEOU) are influential factors

affecting an individual’s willingness to commit to an online community. Additionally,

Lin (2008) empirically tested this theory and found both components of the TAM

theory to influence loyalty within some virtual communities.

While TAM has been a popular theory applied to successful online communities

and membership loyalty, commitment theory also has merit within this area and has

also been found to influence member devotion (Bateman et al., 2010). Commitment

theory states the various types of commitment (continuance, affective and normative

commitment) impacts upon members behaviour within organisations. While this has

traditionally been applied to the management literature to explain volunteers’

dedication to non-profit organizations (Becker, 1960), this also has application in the

online communities literature in an attempt to explain voluntary participation in online

communities. A plethora of studies have demonstrated that online behaviours (such as

frequency of contribution) are somewhat determined by an individuals’ perceived value

from the online community. For example, Wesler, Gleave, Fisher, and Smith (2007)

have illustrated informational benefits from online communities. Alternatively, the

benefits of staying in a community may outweigh the cost of leaving the community if

30

the member believes the community serves a social function and has invested

considerable time establishing an identity (Ma & Agarwal, 2007).

Accordingly, Bateman et al.’s (2010) research supported the notion of three

established forms of organisational commitment. These three forms of organisational

commitment have a unique impact of user behaviour. Firstly, continuance commitment

can predict thread-reading behaviour as members appear to analyse the cost/benefit

ratio associated with the community. That is, one may perceive strong informational

benefits from reading discussion threads, which would ultimately impact upon their

thread-reading user behaviour (Wesler et al., 2007). Secondly, affective commitment

was found to impact upon reply-behaviour. Specifically, those with a strong emotional

attachment were more inclined to help others in the community, thus those with a strong

sense of belonging would feel more inclined to invest time into helping others within

their community (Fisher et al., 2006, Wellman & Gulia, 1999). Equally, this can also

be perceived by others as an act of solidarity amongst the community (Bateman et al.,

2011). On the other hand, those who demonstrate lower levels of emotional attachment

are far less likely to care about how other members perceive them, or indeed care about

the development of the community on-the-whole. Consequently, those individuals in

particular are far less likely to contribute to the community and reply to posts. Finally,

normative commitment, which is the feeling individuals have towards remaining in the

community, predicted only moderating behaviours. Therefore, those members of an

online community who internalised their sense of loyalty, were indeed more likely to

moderate and foster discussions (Kim, Choi, Qualls, & Han, 2010).

In accordance with the previous point, Ma and Agarwal’s (2007) research is

congruent with aspects of continuance community commitment. Their research

denotes that identity communication is a salient characteristic for knowledge

31

contribution in online communities as it is central to social discourse (Wynn & Katz,

1997). Ma and Agarwal’s empirical research assessed the impact of community

infrastructure design and identity verification with regards to computer-mediated

communication. Their systematic theory relating to design and knowledge contribution

states that when members confirmed their salient identities, they were in-turn more

likely to rate their community experience higher and ultimately contribute more to the

community as a whole. This study claims that accurate communication combined with

verification of identity creates an amplified sense of self-worth that serves as a

motivator for continued contribution to the community. However, due to the design of

the study, no causations can be determined (Heiman, 2001; Heiman & Harper, 1999;

Langdridge, 2004).

Ren et al. (2007) have noted the different rationalisation for joining or indeed

staying within a particular group. Essentially, this relates to the distinction between

identity and bonds, identity-based attachment refers to an attachment formed with the

group as a whole; alternatively, bond-based attachments refers to individuals staying

within the group because they are fond of specific members within the group (Back,

1951). Prentice, Miller, and Lightdale (1994) investigated the distinction between these

two interlinked concepts within University clubs. Groups were classified as follows;

topic-based groups such as newspaper and sports team were deemed common-identity

groups and fraternity clubs were categorised as relation-based groups. The results

found that common-identity groups reported feeling a stronger attachment to the group,

rather than any specific members; whereas, relation-based groups reported feeling

attached to both the group and members.

Correspondingly then, this has been applied to online community research

(Postmes & Spears 2000; Sassenberg & Postmes 2002; Utz, 2003; Utz & Sassenberg,

32

2002). In general, common-identity (or identity-based groups) suggests that members

feel a stronger commitment to the online community’s purpose or topic, rather than

attachment to members. While Ren et al. (2007) have also investigated this notion,

their research suggested that group design can “increase the likelihood of mainly bond-

based or identity-based attachment to the group” (p.381, Ren et al., 2007). Moreover,

as online communities are dependent upon communication, it is also likely that the

communication that is exchanged in these virtual spaces is dependent upon the types of

attachment formed.

While Ren et al. (2007) adopt a social engineering theoretical approach to

designing online communities; this aspect of online behaviour has not yet been studied

in-depth. However, based on the available literature it is plausible to assume that

individuals who form bond-based attachments have different motivations for joining

and remaining in an online community in comparison to individuals who form identity-

based attachment. Consequently, those individuals who become popular in identity-

based groups are more likely to discuss and promote topics congruent with the groups’

identity. In contrast, those who form bond-based attachments are likely to engage in

off-topic discussions with friends (Ren et al., 2011).

More recently research focusing on identities and online communities has

begun to focus on group identities, and more specifically the emotional and moral

connections that the online community may offer (Lockwood, 2014; Othmani &

Bouslama, 2015). This is largely due to the fact that communities, which tend to have

stronger group identities, have a higher number of contributions from different

members (Bonacich & Schneider, 1992). This is based on the premise that those who

share a common identity also have similar goals and interests which ultimately impacts

33

upon the information sharing and discussions amongst users (Nahapiet & Ghoshal,

1998).

Zhou (2011) found social identity to have a significant effect on membership

participation. More specifically, this survey-research design demonstrated that social

processes such as identification and internalisation, opposed to compliance, influence

members to contribute to discussions and posts. Equally, Zhou’s findings were

consistent with that of previous literature, which claimed affective identity (or one’s

sense of community) had an effect of social network usage and allows newcomers to

adapt to the community quicker (Zhang, 2010). However, while Zhou and Zhang’s

research is somewhat limited as they are largely based on popular social networking

sites; nonetheless, similar findings have been generalized to online communities.

Indeed, several scholars have denoted that finding a sense of belonging, such as

emotional connections, influence and membership to ultimately affect online user

participation (Lin, 2008; Teo, Chan, Wei, & Zhang, 2003)

Sun, Rau, and Ma (2014) denote a number of factors that affect user

participation in online communities. These factors are “group identity, usability, pro-

sharing norm, reciprocity and reputation” (p. 1, Sun et al., 2014). Accordingly, this

appears to advocate both the TAM and commitment theory relating to online

communities and stress the importance of reputation. While informational needs are

strong motivators for online behaviour (Han, Hou, Kim, & Gustafson, 2013; Nonnecke

& Preece, 2001; Schneider, von Krogh, & Jager, 2013), other motivators include; self-

efficacy (Cabrera, Collins, & Salgado, 2006; Chen & Hung, 2010), the desire to achieve

(Tedjamulia, Olsen, Dean, & Albrecht, 2005), and the need for social capital

(Gulanowski, 2018; Han et al., 2007) and popularity (Utz, Tanis, & Vermeulen, 2012).

However, Fan, Wu, and Chiang (2009) state that people feel compelled to share

34

knowledge in communities to avoid the perception of “free-riding.”

Despite the vast amount of research focusing on contribution in online

communities, research has illustrated that the majority of information available in

online communities is actually only created by a small minority of users. To elaborate

on this point, van Mierlo (2014) found that top 1% most active users created 73.6% of

posts on average. Moreover, it has been documented that every user appears to read

more than they actually post (Ebner, Holzinger, & Catarci, 2005). However, the group

influence of community behaviour has seldom been examined in comparison to the

motivational needs and online community user participation. Additionally, the vast

amount of literature appears to focus on the rationalisations for participating such as

emotive, attachments and identity reasons (Bateman et al., 2011; Ma & Agarwal, 2007;

Ren et al., 2007) when, in fact, it could be the other members deemed “credible” that

influences participation from users and gain momentum for online communities.

Alternatively, member participation could be influence by perceived credibility of the

site. As such, credibility of both online communities and communicators will be the

next focus of the review.

2.1.2. Credibility in Online Communities

As discussed previously, the popularity of social technologies and online

platforms can influence online participation as a whole (Casaló, Flavián, & Guinalíu,

2008; Greer & Jehn, 2009). Consequently, more traditional channels of communication

seem to have lost credibility and individuals appear to be favouring online sources for

perceived accurate information (Scoble & Israel, 2007; Sims, Powell, & Vidgens 2013).

Online consumers make judgments about the credibility and accuracy of information

that they encounter on the internet, but their notion of credibility ultimately affects their

participation and online behavior (Metzger & Flanagin, 2013).

35

2.1.2.1. Credibility and the Internet

More recent accounts of credibility focus on how believable the source and the

message received, rather than the speaker to determine source credibility (Hovlan,

Janis, & Kelly, 1953). Hajli, Sims, Featherman, and Love (2014) coined credibility as

the “believability and trustworthiness of information” (p.2); though, this concept has

also been related to accuracy, reliability and objectivity (Self, 1996). The notion of

credibility has been widely researched and is founded on the belief that “credible people

are believable people, and credible information is believable information” (Hilligoss &

Rieh, 2008, p. 1468). Moreover, this rather multi-dimensional concept has been

associated with two fundamental aspects; trustworthiness and expertise (Hilligoss &

Rieh, 2008; Hovland et al., 1953, Young, Komlodi, Rozsa, & Chu, 2016).

“Trustworthiness refers to the perceived goodness or morality of a source” (Young et

al., 2016, p.14). Whereas expertise has been defined as “perceived knowledge, skill,

and experience of the source” (Fogg, 2003, p. 124). Ultimately, these attributes

determine online users’ perceptions of credibility, which affects a multitude of potential

online behaviours such as attitude change and participation (Zhao et al., 2014).

Research has found that the more relevant the information presented in online

communities, the higher the quality of the content (Hajli et al., 2014). Individuals

concerned with this quality of content have adopted a checklist for online consumers to

help aid them in recognising credible information. Understandably, the characteristics

of the source lead to the assumption that credible sources produce credible information.

Accordingly, consumers are told to examine aspects such as layout, visual quality,

spelling and grammar to establish website credibility in a bid to determine accuracy

(Kim, 2010; Metzger & Hall, 2005). Additionally, research has found another level of

criterion to ascertain credibility and in relation to source sponsor or Uniform Resource

Locators (URLs); thus, individuals are told to examine authority, bias, expertise and

36

reputation to assess objectivity (Glantz, 2000; Greer & Jehn, 2009; Kim, 2010; Metzger

& Hall, 2005). Therefore, this criterion appears to be on different echelons but

essentially all of these elements interact with one-an-other to inaugurate both credibility

of information and credibility of source (Fogg et al., 2003; Hong, 2006; Kim, 2010; Liu

& Huang, 2005;). Equally, word length has been found to be an indicator of perceived

quality and impact upon credibility with regards to information needs (Agichtein et al.,

2008).

Moreover, Greer (2003) found that source credibility was significantly

correlated with story credibility, thus, demonstrating that rather than relying on

advertising cues, individuals use their existing knowledge to assess online information

and focus on the information in the content to determine credibility. Consequently,

advertising is not necessarily correlated with source credibility and online community’s

members tend to use alternative online sources of information as trusted sources

(Scoble & Isreal, 2006; Sims et al., 2013). Due to this individuals are increasingly

empowered to generate their own content of information (Hajli et al., 2013). Thus,

there is an emerging body of research on knowledge sharing in online communities

which this review will examine later.

There appears to be huge discrepancies with regards to how individuals evaluate

credibility online. Eysenbach and Kohler (2002) examined how consumers appraise

and rate health information online. Their content analysis revealed that authority

opposed to information or source was a more prominent determinant of credibility in

information online, therefore, opposing the notion that credibility is compiled of source

and information online (Metzer & Flanigan, 2013). Alternatively, Reih (2002) found

that scholars were far more concerned with source reputation and content rather than

visuals. Contrastingly, Fogg et al. (2003) found that the average consumer based their

37

credibility rating upon visual cues (46.1% of the population sample) and superficial

information rather than the content on website; though reputation was also a prominent

indicator or credibility evaluations. Hong (2006) found students rate message features

(quotations and referenced sources) as more important than attributes of the message.

Yet, Liu and Huang (2005) claim that authors name, reputation and affiliation are

important.

Evidently, the importance of credibility varies hugely due to the characteristics

of the research. In Reih’s (2002), Hong (2006) and Liu and Huang’s (2005) their

research relies solely on students as participants, which could account for the

significance of reputation being at the heart of their credibility rating. Alternatively,

Fogg et al. (2003) examined the average internet user, which could provide some

insight on these discrepancies. Nonetheless, when amalgamated these findings seem

to suggest that expert or skilled computer literate individuals place emphasis on the

content to ascertain credibility.

2.2. Communicator Characteristics

As exhibited in the above literature, credibility alongside quality and overall

representativeness is one of the criteria found to influence attitudes (Sundar, 1999).

This is especially prominent when individuals assess media information (Greer, 2000).

Those within media fields have stated that a credible source is one that is seen as

providing correct, bias-free information (Hass, 1981). As mentioned previously,

trustworthiness and overall expertise are considered the most important aspect to

evaluating source credibility (Greer, 2000). Indeed, Milburns’ (1991) research pertains

this point in that information which was rated as high-expertise led to a greater change

in attitude when compared with low-expertise source content which found no changes

in attitude in the message received. However, Milburn also found that overall

38

trustworthiness of the “communicator” is equally as important as their expertise.

Seemingly, bias-free objective communicators are also essential in evaluating the

credibility of a message/content to avoid apparent resistance to message persuasion

(Greenburg & Miller, 1966; Metzger & Hall, 2005). Consequently, there needs to be

a more substantiated body of literature surrounding communicators and what makes an

individual appear credible and whether this impacts upon participation in online

communities.

2.2.1. Opinion Leaders

2.2.1.1. Origin of the Opinion Leader

In respect to the latter point, a growing body of research has investigated the

necessary components of persuasive speakers or those that appear to exert the most

influence (Gass & Seiter, 1999). Indeed, this has become ever more important in the

online world due to the potential for marketers to maximise efforts in online

communities by finding those with the greatest influence and highest levels of

credibility (Guldbrandsson, Nordvik, & Bremberg, 2012). Opinion leaders or

‘influentials’ have a primary role as they filter information from the media to their

followers (Joinson & Dove, n.d.). Katz and Lazarsfield (1957) proposed a ‘Two Step

Communication Model” whereby ‘opinion leaders’ or [influentials] “act as

intermediaries between the mass media and the majority of society” (Watts & Dodds,

2007, p.441). Opinion leaders are individuals who are most likely to influence others

in their “immediate environment” (Katz & Larsfield, 1957, p.3) as demonstrated in the

figure below.

39

Figure 2.1. Two-Step Model of Influence. Adapted from “Influentials, networks and

public opinion” by D., J. Watts, & P., S. Dodds, (2007), Journal of Consumer Research,

34(4), 441-458. 1

However, these individuals are not necessarily leaders in the usual sense of the

word like someone in a position of authority, rather, this is a person-to-person

“ordinary” informal influence whereby select individuals are highly informed and

connected; thus, diffuse the message to their ‘followers’ (Goldsmith, 2004; Grewal,

Mehta, & Kardes, 2000;Watts & Dodds, 2007). Put simply, David Beckham is not a

CEO or World leader, but has a level of interpersonal influence, which is why

corporations employ him for marketing strategies. This is the level of influence one is

talking about for the present review.

Opinion leaders have been found to be important with regards to the success of

health interventions (Valente & Pumpuage, 2007). Kelly et al. (1991) found engaging

opinion leaders popular with gay men ultimately served as behaviour change endorsers

1 1Figure shows the diffusion of information from media (TV) to various sources. The stars

represent opinion leaders who are well connected to others and transmit the information.

40

and unsafe sex practices decreased. More so, Lomas et al. (1991) found opinion leaders

to be successful at decreasing the rate of caesarian births while Earp et al. (2002) found

opinion leaders increase in communitywide mammography use. Evidently, opinion

leaders serve as a role model for conveying health messages and are crucial in message

adoption which creates a positive outcome with regards to changing the opinions and

behaviours of the masses (Doumit et al., 2006; Flodren et al., 2011; Joinson & Dove,

n.d.; Kelly et al., 1991, Lomas et al., 1991; Weimann, Trustin, Vuuren, & Foubery,

2007).

2.2.1.2. Identifying Opinion Leaders

Based on the potential that opinion leaders have with regards to behaviour

change, it is not surprising that scholars have invested considerable time to identifying

these influentials. Rogers (1962) claim opinion leaders have a number of typical

characteristics a) a high level of social participation b) high social status within a given

community and finally c) high levels of social responsibility. Moreover, research has

gone a step further and found that opinion leaders are more innovative and are

particularly skilled or knowledgeable within a certain area (Childers, 1986;

Venkatraman, 1990; Weimann, Tustin, van Vuuren, & Joubert, 2007). Accordingly,

Zhang and Dong (2008) state “[opinion leaders] can obtain information by more than

one channel and have rich life experiences. They [opinion leaders] are knowledgeable

and professional in one specialty. They can contact innovation agencies frequently and

they prefer to take part in formal and informal social activities. They have a wide social

relationship and connect with the public closely, have far-reaching insight and an

eagerness to accept new things” (p. 22).

Broadly speaking, psychometric measures have found mixed results when

assessing items relative to opinion leaders (Weimann, 1991; Weiman et al., 2007).

41

However, both Troldahl and van Dam (1965) and Booth and Babchuk (1972) claim

opinion leaders report a high level of gregariousness in comparison to their

counterparts. Additionally, opinion leaders have a higher level of personal involvement

in public affairs, educational matters and family planning (Heath & Bekker, 1986;

South-Winter, Dai, & Porter, 2015). Moreover, Rieken and Yavan (1983) ascertain

that such individual leaders (which are arguably more influentials than leaders) are

characterised by certain sociographic measures. More specifically, they attend far more

social events such as church, plays, concerts and campus activities (Heath & Bekker,

1986; Rieken & Yavan, 1983; Weimann, 1994). Though, these findings have recently

been disputed, as research has opposed these notions of sociability claiming that

opinion leaders do not necessarily occupy formal positions within a society or

community and it is their expertise that denounces their role (Danesi, 2013; Kwon &

Song, 2015; Winter et al., 2015).

Equally, age, gender, marital status and other socio-economic statuses have also

received mixed results thus far. Lee (2010) and Vishwanath (2011) posit that

individuals are shaped by the opinions and perceptions of others and as such marital

status and age do not appear to matter much in the influential-followers relationship.

However, Sarathy and Patro (2013) found opinion leaders to be younger and have a

post-high school level of education and high occupational statuses. To add further

confusion, Williams and Duvel (2005) found opinion leaders to have a relatively low

level of education. Evidently, the ways in which opinion leaders are characterised has

resulted in various inconsistencies, which is not advancing knowledge on how or why

these specific individuals are regarded with such importance in the dyadic leader-

follower relationship.

42

Despite the large body of research on opinion leadership, identifying

appropriate ‘leaders’2 for practical purposes is somewhat of a challenge for researchers

(Weimann et al., 2007). Traditional methods for identifying opinion leaders included

observation, grading key roles, social interpersonal relationship measurement and self-

reports/identification. However, on the internet such characteristics are not necessarily

available due to the anonymous nature of the Web (Kang, Brown, & Kiesler, 2013).

As such, researchers have begun to determine new ways to identify these influential

individuals in online communities (Bakshy, Hofman, Mason, & Watts, 2011; Cha,

Haddadi, Benevenuto, & Gummadi, 2010; Gilwa & Zygmunt, 2015; Kawk, Lee, Park,

& Moon, 2010; Jungnickel, 2018; Weng, Lim, Jiang, & He, 2010).

2.2.1.3. Identifying Influentials in Online Communities

Several attempts have been made throughout the literature to track influence in

social mediums such as Twitter (Bakshy et al., 2011; Cha et al., 2010; Leavitt,

Burchard, Fisher, & Gilbert, 2009); consequently, academics have examined the spatial

and social dynamics of top influentials on social networking sites. These influential

individuals are targeted for research purposes as they are particularly skilled in

persuading others (Rogers, 1962). As such, communication theory states that by

targeting these top influentials it will produce a cascade of social influence and word-

of-mouth (WOM; Katz & Lazarsfeld, 1955).

Cha et al. (2010) investigated the dynamics of user influence across time, this

study investigated three measures of influence: in-degree (how many followers an

individual has), retweets (how much an individual is mentioned by other users) and

mentions (how much one is mentioned to engage others in conversation). They found

2 These specific individuals are referred to as ‘leaders’ in the research articles

examined

43

that influence is not gained spontaneously, but rather through a concerted effort.

Additionally, their results demonstrated that those who have the most followers online

may not necessarily get mentioned by others the most.

Alternatively, Aral, Muchkin, and Sundararajan (2009) measured influence

through mobile services adoption over Yahoo Messenger. Whereas, Kawk et al. (2010)

determined influence by three measures; the number of followers, the number of

retweets and the PageRanking. Similarly, Weng et al. (2010) assessed influence using

a comparable measurement; PageRank, modified PageRank and number of followers.

Finally, Goyal, Bonchi, and Lakshmanan (2010) identified influence via “leaders” from

Yahoo! Movie users. As illustrated above, the vast amount of research conducted on

Twitter has aimed to define influence and identify those who exert the most power over

others. However, research has ascertained that those deemed as influential users is

dependent upon the influence measurement employed by researchers (Kwak et al.,

2010; Weng et al., 2010).

Despite the limitations of previous literature, some consistent results have

emerged with regards to attempting to measure influence. As such, the characteristics

of influential users generally demonstrate that these individuals have personality traits

such as expertise and credibility (Gladwell, 2002). Likewise, these users have network

attributes such as connectivity and centrality among followers (Anger & Kittl, 2011;

Bakshy et al., 2011; Cha et al., 2010). Though, popularity amongst users (number of

followers) or “indegree influence” has produced mixed results (Cha et al., 2010; Kwal

et al., 2010). Anger and Kittl (2011) state that the number of followers a user has reveals

very little about their influence, thus, claiming the quality of the relationships (as

mentioned in online community user participation) are more important than the number

44

of followers when identifying those whom exert influence. As such, this can only be

achieved through in-depth analysis, rather than simple quantification.

Agarwal, Lin, Tang, and Yu (2008) examined influential bloggers in a

community but differentiated between those influential bloggers that are active and

influentials that are inactive. Indeed, they discuss the challenges in identifying

influentials that are active in a fixed amount of time as posting frequency may be

posting of “junk” or irrelevant material. Nonetheless, their criteria for identifying

influential individuals included post length, average length of comments per post,

number of comments per post, length of time and number of interlinks. However, while

Agarwal et al. (2008) note that influentials are not necessarily fixed in time, their study

was initially only conducted over a 30-day window in line with monthly top blogger

ratings for influential websites, then later their preliminary model was applied to a

moving 30-day window to examine those deemed influential. Thus, there may be many

other influentials that have been overlooked in this investigation. Though, there was a

concerted effort to distinguish between those that were active and those that were not

which previous quantification may not have recognised or accounted for. Nevertheless,

this study did not examine why individuals may have lost potential influence which is

where the present research will advance knowledge.

Huffakers’ (2010) attempt to characterise the traits of online influentials

examines both social influence and leadership; thus, utilising both terms reciprocally.

While this has been examined through a brief discussion of opinion leadership, it cannot

be determined whether this study is actually assessing leaders, or merely influential

individuals of online communities as both are coined together. Therefore, by briefly

including influence into the definition, influence and leadership are then referred to

simultaneously and there is no attempt to differentiate the characteristics of influentials

45

from the online characteristics associated with online leaders. Therefore, making this

relevant for the present review. With regards to the aforementioned definition, opinion

leaders do occasionally display leadership behaviours; however, research has stated that

opinion leaders are not necessarily figures of authority (Soumerai et al., 1998).

Moreover, opinion leaders influence is largely derived from their social status and

perceived influence (Watts & Dodds, 2007), thus, rationalising including opinion

leaders into the present review.

Huffaker (2010) analysed 16 Google groups that encompassed more than

600,000 messages over a two-year period. He found that number of posts, number of

replies and tenure were all indicators of influential individuals in online communities.

Equally, network centrality was also found to be a positively related. Overall, Huffaker

found talkativeness, affect, credibility, assertiveness and linguistic diversity to be

positively associated with online “leaders” or potential influentials. Equally, they

display more confidence in their posts. However, Huffaker did not include those that

had previously been in the community for some time and then become inactive early in

their 20-month time frame. Therefore, there is some misrepresentation in their sample.

As Agrawal et al. (2008) have previously displayed; not all influentials are those that

are active; thus, some element of behaviour may have been overlooked which this

research will remedy.

Correspondingly, Zhu, Haiyi, Kraut, and Kittur (2012) investigated the

effectiveness of distinct ‘leader’ behaviours on Wikipedia. Their classification of

leaders was determined by the messages left on editors’ pages. More so, their

measurement of effectiveness was determined by the extent to which others were

affected and contributed. This work identified different types of language to be

associated with different leadership styles.

46

Predictors of emergent influentials online appear to vary somewhat, depending

upon the scholar. Research has ascertained that sociability, responsibility, cultural

values, performance and trust are indicators of perceived “leaders” online; while others

claim that the amount of communication is essential in identifying online influentials

(Cassell, Huffaker, & Tversky, 2005; Cassell, Huffaker, Tversky, & Ferriman, 2006;

Greer & Jehn, 2009).

In accordance with the latter point, Yoo and Alavi (2002) find emergent

“leaders” send a higher quantity of emails in comparison to their counterparts. These

emails are often longer in length and more task-orientated (Yoo & Avali, 2002; 2004).

Wickham and Walther (2007) ascertain this notion and assert that high levels of

communication were consistent with a group member displaying influential

behaviours. Alternatively, Cassell et al. (2005) denotes that those emerging as

influentials use more social processes. Whereas Greer and Jehn (2009) found flattery

to have advantageous outcomes with regards to gaining support as a soft tactic

influence. This indicated that those that emerge as “leaders” possess a certain mentoring

quality in their online utterances. Taken together, these studies illustrate that the quality

of the conversation is as important as the frequency when investigating those that have

influence online.

2.3. Language and Authority

As noted by Cha et al. (2010) influential status is earned in online communities.

Evidently, influential individuals communicate more than their counterparts (Yoo &

Avali, 2004), but the research presented also indicates that it is not just activity levels

– the quality of the communication is also pivotal in computer-mediated environments.

Balthazard, Waldman, and Warren (2009) examined the etiology of “leaders” in virtual

teams and find linguistic quality and grammatical complexity to be significant

47

predictors of “leadership” emergence. Moreover, Cassell et al. (2006) examined 48,000

messages and found emergent “leaders” use more “we” words, use more social

processes to encourage dialog and ask more questions (Who? What? Where? When?).

Therefore, they have an apparent group identity and foster discussion amongst

members. Equally, while they use a large sample, they include adolescent or

undergraduate populations; thus, this cannot be applied to the average internet users as

younger students may be more resilient to language diversity and more prone to

answering questions.

On the other hand, Joinson and Dove (n.d.) examined two online communities

and comprised a sample of 245 “leaders” and 353 “leaders” retrospectively (with a

matched sample of non-leaders for each community). Interestingly, from using this

sample their results contradicted that of Cassell et al. (2006), in that they found

influentials of both communities to use significantly less first person pronouns (“I”),

and ask less questions. Yet, they found influentials of just one of the communities to

use fewer first person plurals than their counterparts (‘we”). Interestingly however,

influentials from both communities did use more second person pronouns (“you”).

Mixed results also emerged with regards to the use of emotive language; one

community appeared to broadly support the notion that influentials use more emotive

language; whereas, the second community found no difference in the use of emotive

language between influential and non-influentials. Accordingly, this may relate back

to the research previously presented and the notions of attachments and bond in online

communities (Bateman et al., 2011; Ma & Agarwal, 2007; Ren et al., 2011). In other

words, different communities may have different attachments, which needs to be

investigated to enhance knowledge on participation. Therefore, participation may be a

result of both influentials and the bonds that are formed. This research intends to

48

respond to this through investigation of online communities via a combination for

methods.

Together Joinson and Doves’ (n.d.) research appears to highlight the uniqueness

of online communities. In that different community influentials have a different pattern

of behaviour. There was a higher level of agreements and a lack of sense of community

in the religious online community; leading to more conversation about home life.

Alternatively, results revealed that negative emotive words attained some degree of

status in the second community. As such, while Joinson and Dove and Cassell et al.

(2006) have provided valuable insight into online community identity and authority,

they are not without limitations. Namely, their investigation addresses word frequency

through Linguistic Inquiry and Word Count (LIWC) rather than semantic meaning and

omits exact meaning of utterances online (Pennebaker, Mehl, & Niederhoffer, 2003).

While this research has provided some clarity regarding identifying influentials online,

it is evident that some of the potential indicators of influentials in online communities

vary depending upon the community. As such, the markers of so-called influentials

may only be ascertained via detailed qualitative enquiry.

As exhibited above, there is a clear issue in the literature identifying influentials

in online communities; though, there is clear evidence that there are certain behavioural,

social and linguistic features that aid identification of those that possess influence over

others in online communities. However, a greater understanding of the features, tactics

and language that influential individuals use cannot be obtained without an exploratory

and in-depth investigation. Additionally, research must acknowledge the fluidity of

influentials and role transitions that occur in online communities as influential

individuals are not fixed in time.

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2.4. Rationale for Present Research

Agrawal et al. (2008) claim influential bloggers are not necessarily those that

are active. Indeed, most sites advertise top bloggers within time frames such as monthly

top bloggers. However, this would omit those that are not active within certain time

frames. With the speed of the internet and rapid growth of online communities, it is

important to address influential individuals that are not fixed in time and conceptualise

how and why they become influential within a given community. From that, research

would need to address and utilise all of the methods currently available to identify

influentials and provide a richer, more pragmatic account of the behaviours these select

few individuals ascertain. While Agrawal et al. (2008) and Joinson and Dove (n.d.)

have used historical data and applied their identification techniques to those that may

no longer be active, they have not also examined reasoning behind why individuals

subsequently lose influential status within a given community. Quantification may not

be the way forward to address the fluidity of roles within these online communities.

An in-depth understanding must be provided to examine the transition from follower to

influential (and vice versa) as these individuals do hold permanent influential status,

they can become inactive or revert back to a less important role. Equally, drawing from

the previously discussed work from Cha et al. (2010), influence or indeed influentials

are developed over time with considerable effort. Therefore, this can only be explored

in-depth through qualitative exploration.

2.5 Theoretical Framework: Social Identity Theory

2.5.1. Social Identity

Recently, organisational literature has seen the emergence of Social Identity

Theory (SIT) as a perspective on understanding the organisational context and social

environment in which groups develop and function (Haslam et al., 2006; Hogg, Terry,

50

& White, 1995; Pegg, O’Donnell, Lala, & Barbar, 2018). This is derived from the

notion that identification within particular groups ultimately leads to more cooperation

and a higher level of productivity and performance (Kimble, 2011; Kimble, Li, &

Barlow, 2009; Jimenez, Boehe, Taras, & Caprar, 2017). Therefore, drawing from the

work of Tajfel and Turner (1979) and developments within social psychology, social

identity presents a framework for the present study within online communities as online

communities play an important role in how online community members enact their

social selves online (Pegg et al., 2018).

Stryker and Burke (2000) claim three distinct uses of identity which apply to

online communities and online selves. Firstly, an identity may refer to a culture;

secondly, a common identification within a social category and lastly, identities are

entwined meaning that people attach to the many different roles that they may play.

Tajfel (1974) defined social identity as “an individuals’ knowledge that he belongs to

a certain social group together with some emotional value and significance to them of

this group membership” (p.292). In addition, this social identity can be perceived as

ones position within a social structure as groups impact on an individuals’ sense on

belonging (Hogg, 2004).

As discussed earlier in the review, Fisher et al. (2006) found individuals invest

more time into an online community when they have a strong sense of self. Equally,

Kim et al. (2010) and Ren et al.’s (2011) work, together found that popular individuals

have a stronger loyalty to their online community and promote topics consist with the

groups’ identity; yet, there appears to be a caveat within the online community research

addressing the online identities of these influentials despite their clear influence over

other online community members and usefulness to online discussions (Aggarwal et

al., 2008; Aral et al., 2009; Cha et al., 2010; Fisher et al., 2006; Ma & Agarwal, 1997;

51

Ren et al., 2011; Weng et al., 2010). As such, their identities seem key in understanding

contributions in online communities (Ma & Agarwal, 1997).

SIT posits that an individual can express an awareness of their social category

and the value they feel towards that membership classification (Hogg, 2004). Due to

this, groups also have a huge impact on an individual and their identity, and who

influences the group that they feel a sense of belonging to (Hogg et al., 2004); thus,

showing theoretical relevance to the present study when identifying those who are

particularly influential within a community and how their social identification may

impact on their online behaviour. This perception of self-belonging and group

membership is known as self-categorisation (Turner, 1982) This means that when an

individual perceives themselves as being part of a certain category or group it

subsequently enhances feelings of self-esteem, as groups wish to have positive

identities (Stets & Burke, 2000). This asserted categorisation may drive an individual

to behave differently towards in-group and out-group members (Akba, 2015). In other

words, the more an individual identifies with a particular group, the more likely they

are to make attributions to their group (in-group) and subsequent discriminations

towards those not in their group (out-group; Branscombe et al., 1999; Operario & Fiske,

2001). Therefore, the way in which influential individuals behave within their online

communities is of great interest but has often been examined via experimental designs

opposed to naturally occurring groups (Abbink & Harris, 2019).

2.5.2. Social Identity and the Internet

Jenkins (2004) posits that all human identities are by definition social identities

and that it is this identity which concerns how we identify our similarities and

differences from other groups of individuals. Indeed, it is this interplay between how

we identify with ourselves and how others identify us which the social identity

52

approach examines (Code & Zaparyniuk, 2010; Jenkins, 2004). Given that the internet

allows individuals to explore multiple identities and adopt various roles (Korte, 2007),

the internet and online communities offers a virtual space where individuals can address

their need to belong and develop their social identities (Gangadharbatla, 2009;

Vernuccio, Pagani, Barbarossa & Pastore, 2015).

Research has frequently considered the effect of anonymity and the change in

social interaction due to this (Turkle, 1999; Wellerman et al., 2001), and the positive

and negative effects of a reduction of psychological constraints (Joinson, 2007;

Lapidot-Lefler & Barak, 2015; Pegg et al., 2018). However, Zajacz (2013) suggests

that this over focus on the use of anonymity has meant that research into the importance

of user identity have been overlooked. Recent research on the online identity

phenomena claims that the online identity is central to understanding intrapersonal

relations and how this influences real world events (Pakhyusiva, 2019). Moreover, that

online identities can indeed help understand one’s self more fully (Zhichkina &

Belinskaya, 2019). The present research uses ideologically driven communities and

proposes that such communities are in fact opinion-based group – as these collections

of online users share similar beliefs, opinions and ideologies (Bliuc, Betts, Vergani,

Iqbal., & Dunn, 2019; Bliuc et al., 2015); thus, drawing from the previously discussed

online identity research to incorporate a better understanding of how individuals

become popular (Cha et al., 2010; Fisher et al., 2006; Ma and Agarwal, 1997; Ren et

al., 2011).

2.5.3. Group Processes

Many theories have tried to explain group behaviour (Haslam et al., 2006;

Harris, 1995; Hogg, 1987; Kolbe & Boos, 2009; Tajfel & Turner, 1979). However,

this research will focus on the social identity approach. The social identity approach

53

has three core element involved in the study of intergroup behaviour; first, is one’s

psychological processes that lead to a social identity; second, strategies that are

involved in maintaining a positive social identity and lastly, key characteristics of the

social structure (Everett, Faber, & Crockett, 2015). The social identity approach has

focused on how individuals can enhance their social identities via categorising

themselves as a group member; known as self-categorization (Turner, 1982). Indeed,

this theory denotes that individuals, or in this case, online community members, must

associate themselves with those with similar beliefs to identify as part of a group or

category (Abrams & Hogg, 2001). Subsequently, individuals come to perceive the

world as groups; ingroups (where an individual is a member) and out-groups (where an

individual has no membership). When individuals internalise their group member, this

then becomes part of the self and those characteristics of the in-groups become part of

the individual’s sense of self (Smith, Coats, & Walling 1999).

Research has also ascertained that ultimately in-group members are more

influential than out-group members and a collective identity can depersonalise the

individual as they would rather belong to a collective identity (Code & Zaparyniuk,

2010). Equally, those who identify highly with the group think of themselves as in-

group members to feel more connected to other members of that group (Everett et al.,

2015). This is often done via comparison and striving to maintain a positive collective

identity; as such, groups often preserves positive social identities by evaluating one’s

own group more favourably than the out-group (Tajfel, 1982). However, from a

theoretical point of view, online interaction and longitudinal data on how individuals

develop their collective identities is not very well understood, nor is there a vast amount

of research examining whether group members perceive a social order within online

groups (Bliuc et al., 2019; Everett, et al., 2015; Turner & Reynolds, 2001), as research

54

has focused more on self-presentation, commitment and participation in online

communities (Schwammlein & Wodzicki, 2012).

2.5.4. Limitations of Social Identity Theory

While a plethora of research has been conducted on SIT (Tajfel & Turner, 1979)

and the benefits of self-categorisation with regards to self-esteem, it is important to

point out that there is limited knowledge on a groups status and perceived power or

authority (Abrams & Hogg, 1988; Hogg & Abrams, 1990). Consequently, more

research is needed into identity and social structure (Postmes et al., 2005; Reicher,

2004). Therefore, at present, there is a caveat in research addressing the identities of

popular or influential individuals online, despite research denoting how central identity

is to contribution in online communities (Ma & Agarwal, 1997).

Equally, the majority of the research conducted on SIT and online identity has

been investigated in respect to virtual teams, which are task orientated and motivated

in nature (Kimble, 2011). Therefore, SIT needs to be examined in a naturally occurring

groups such as an online communities rather than an assigned groups. As such, the way

social identity is formed and then influences behaviour cannot necessarily be

understood at the assigned group level (Wei-Au, 2010). Equally, SIT has mostly been

examined in a static perspective (i.e. social identities are fixed), whereas more recent

research has come to see social identity as a fluid process as people can switch between

identities and roles (Champniss et al., 2015; Vernuccio et al., 2015).

2.5.5. Contribution to Theoretical Framework

This research offers a number of contributions to SIT (Tajfel & Turner, 1979);

firstly, an in-depth analysis in naturally occurring, longitudinal data from two far-right

online communities with a strong ideological foundation to foster a strong sense of self

belonging and membership. Indeed, this research is the only research to have examined

both the identification markers of influential or authoritative online community

55

members, as well as qualitatively exploring the nature of their social identities in

naturally occurring data. Therefore, when individuals have emerged as authoritative

or influential users within a particular online forum, do they display more self-

categorisation? Also, when they lose such status do they display less evidence of their

social identities? Could this be a factor in why one loses influence amongst their peers?

This also adds knowledge by exploring the social identities of influential community

members during their community tenure and exploring how their notions of loyalty,

identity and group membership are promoted and exhibited in their interactions with

others.

2.6. Research Questions and Aims

R1: How do influential users interact with others?

R2: What online behaviours do influentials display that distinguishes them from

followers?

R3: What are the online behaviours that inspire their rise to power?

R4: How do influential individuals lose their power status?

R5: Explore online identities of influentials

2.7. Contribution to Knowledge

Evidently, the literature available on online influentials suffers from limitations,

which is preventing research advancement in this area. As such, the contribution of

this research is to examine in-depth, influentials in online communities that are not

fixed in time and utilise a multitude of techniques (qualitative and quantitative) to

explore influential online behaviour. By amalgamating a variety of methods a greater

understanding of the influence process that occurs online can be achieved.

Additionally, by exploring online communities and investigating those

individuals who have the capacity to influence the opinions and attitudes of others

56

through online mediums, this research will highlight the power processes that occur

online. In addition, the second aim of this research is to identify what makes these

individuals influential and subsequently what happens to their social identities when

they are losing their gaining/influence? Finally, this research aims to determine the

social status in online communities with respect to potential influence patterns and

frameworks and explore the online identities of these specific individuals.

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CHAPTER THREE

METHODOLOGY

3.1. Chapter Overview

This chapter describes the methods used to study influential behaviour in online

communities. The research adopts a mixed methods design consisting of two phases;

the first phase of the research utilised quantitative methods to assess the associations of

various variables on users’ reputation and online status. Secondly, the next empirical

phase comprised a narrative analysis to generate a deeper understanding of the

characteristics of influential behaviour and the discourses that occur online.

However, to accomplish the proposed aims, and increase reader transparency,

an overview of the philosophical and methodological choices made throughout this

research will be highlighted and discussed. As such, the beginning of this chapter will

provide an overview of the philosophical dimensions associated with this research,

whilst the latter part of this chapter will reveal the research outline and design.

3.2. Paradigms of Social Research

Paradigms have been discussed as a “cluster of beliefs” or simply “world

views”. These “world views” guide how the research should be conducted and how

various realities should be researched (Corbetta, 2003; Creswell, 1998; Creswell, Clark,

Guttman, & Hanson, 2003). In other words, a paradigm is an assumption about how

particular issues surrounding the research should be investigated.

There are different styles of research which are linked to the various

philosophical stances, for example, positivism is associated with experimental designs,

whilst interpretivism is often associated with interviews (Crotty, 2003; Henn,

Weinstein, & Foard, 2006). Moreover, the method or way of data collection procedure

is largely predetermined by the epistemological position (discussed later). Therefore,

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according to Bryman (1989) “society exhibits contrasting paradigms about the nature

of social reality and about what is acceptable knowledge concerning that reality” (p.

248). In this way, the division of qualitative and quantitative research is not merely a

matter of alternative approaches and methods, but rather this is to do with the very

nature of the subject under investigation, thus, researchers cannot use methods outside

of the paradigm that they are working within. See table below for summary.

Table 3.1

Characteristics of the Basic Paradigms of Social Research

Positivist View Interpretive View

Ontology Naïve Realism – Social

world is “real”, one stable

reality

Constructivism – world

built up of multiple

realities

Epistemology Dualism Non-dualism

One truth exists Different people have

different experiences

(many realities)

Methodology

Methods Quantitative Qualitative

Strategy Mostly deductive Mostly inductive

Experimental –

manipulation

Interviews

Analysis by “variables” Analysis by “cases”

Data Measurable data Descriptive, explanatory

text

Note: characteristics of the basic paradigms of social research. Adapted from “Social

Research” by P. Corbetta, 2003, in Social research: Theory, methods and techniques.

London, England: Sage Publications.

3.2.1. Ontology

Ontology refers to the nature of social phenomenon as it is concerned with the

form of social realities (Corbetta, 2003; Denscombe, 2009). Indeed, ontology has

inspired a number of debates; however, central to such debates are two basic positions;

realists and constructivists. Essentially, realists perceive the social world to be an

objective reality, which exists regardless of whether an individual believes in its

59

existence or not. Alternatively, constructivists regard the social world as a creation.

Therefore, individuals create their own social reality through their actions, values and

beliefs (Crotty, 2003; Denscombe, 2009).

The main distinction between the two positions is that the latter perspective

believes in multiple realities; whereas realists believe in one stable reality. In other

words, “the existence of an idea in the mind tells us nothing about the object in reality”

(Corbetta, 2003, p.12). Therefore, ontology is concerned with what an individual

believes is their social reality (Blaikie, 2009). As such, reflection about these issues

helps researchers clarify the theoretical position.

3.2.2. Epistemology

Indeed, claims about what exists ultimately leads to questions about how it

exists, thus, onto epistemological issues (Hughes, 1990). Grix (2004) claims

“epistemology focuses on the knowledge gathering process and is concerned with

developing new models or theories that are better than the competing models (p.27)”.

As such, there are a number of competing epistemological perspectives that coincide

with the various ontological positions. However, the epistemology inherent in the

theoretical perspective for the present research is pragmatism. Pragmatism is not

necessarily dedicated to one single system of philosophy as researchers are free to

choose the methods, techniques and procedures that best fit their research question(s)

(Creswell, 2003; Morgan, 2007; Murphy, 1990). Thus, pragmatism provides a middle

position philosophically which is largely focused on the problem being studied, rather

than methods (Creswell, 1998; 2003).

3.3. Present Research Design

This study used a mixed methods design to capture the best of both quantitative

and qualitative approaches (Creswell, 1998). This is a method for “collecting and

60

analysing both quantitative and qualitative data in a single study” (Creswell, 2003, p.

24), thus, by mixing the data, a better understanding of the phenomena is produced than

relying solely on a single dataset. Mixed methods maximises strengths and minimises

the weaknesses of singular qualitative and quantitative approaches (Creswell et al.,

2003).

The rationale for integrating the two methods is simple, neither qualitative nor

quantitative methods are sufficient to address the purpose of this research. Therefore,

when using a combination, qualitative and quantitative approaches ultimately allow for

a more complete analysis of the status quo that occur online (Greene, 2007). By

adopting this strategy, a more holistic depiction of online identities and virtual

communities can be provided (Creswell, 2003; Creswell et al., 2003; Tashakkori &

Teddlie, 1998), which is currently omitted from the literature.

Traditionally, quantitative research involves an over-reliance by the researcher

on numerical data. Primarily, quantitative approaches are concerned with magnitude

and measurement (Langdridge, 2004). Therefore, this was used for the first phase of

the research to highlight areas for further exploration and determine the influential

individuals for the qualitative investigation. Qualitative research is concerned with the

quality of the phenomena, thus, there is a greater focus on inductive, rather than

deductive research (Creswell, 1998; Langdridge, 2004). In other words, “qualitative

research consists of a set of interpretative, material practices that make the world

visible” (Creswell, 2003, p.42). Therefore, qualitative research, in comparison to

quantitative measurement, is concerned with the meaning or account of some event or

unit from the participants’ perspective (Creswell, 1998; King, Keohane, & Verba, 1999;

Langdridge, 2004; Maxwell, 2005). As this research is analysing both interactions and

communications for influentials in online communities, this approach is more

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appropriate for the second phase of the research to give further depths to the information

provided from phase one.

When designing the study, a number of issues were considered, these are as

follows; priority, integration and implementation (Creswell et al., 2003). Firstly,

priority refers to which aspect of the study will receive the most emphasis; qualitative

or quantitative methods. Secondly, implementation refers to the order of the phases in

the research; that is, is the data collected sequentially or concurrently? Finally,

integration is the combining of the data. For this research, an exploratory, sequential

design has been utilised as the methods are implemented consecutively (Creswell,

2003).

3.4. Research Outline

This research consisted of two distinct phases. The first phase of the research

used a quantitative approach to examine groups of users and their behaviours in online

communities. This, in turn, informed the qualitative aspect of the research and allowed

a more in-depth analysis examining behaviours through an exploratory method (see

detailed discussion below). Specifically, quantitative approaches have illustrated an

overview of certain behavioural characterstics associated with those who were

influential; whilst qualitative methods facilitated greater inquiry into the language,

identities and interactions with followers. Ultimately, this provides a more holistic

image of online behaviours and adds greater knowledge acquisition. The table below

discusses the research questions and hypotheses in conjunction with the two phases for

this thesis.

Table 3.2

Research Questions/Hypotheses for Research Outline

Phase One:

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Quantitative Database

H1 – Influential individuals will have a higher number of posts in comparison with their

counterparts (Greer & Jehn, 2009; Huffaker, Tversky, & Ferriman, 2006)

H2 – Influential individuals will have a higher number of threads in comparison with

their counterparts (Greer & Jehn, 2009; Huffaker et al., 2006)

H3 – Influential individuals will have a higher word count (per post) than their

counterparts (Huffaker et al., 2006)

H4 – Influential individuals will have a higher word count (per thread) than their

counterparts (Huffaker et al., 2006)

H5 – Influential individuals will have a higher number of thanks from other community

members than their counterparts (Joinson & Dove, n.d.)

H6 – There will be a relationship between posting frequency and status (Greer & Jehn,

2009)

H7 – There will be a relationship between thread starting frequency and status (Bakshy

et al., 2011)

H8 – There will be a relationship between acknowledgement from others and status

(Cha et al., 2010)

H9 – Influential members of IU will have more connections to other members in

comparison with influential members of LWP (Anger & Kittl, 2011; Bakshy et al.,

2011; Cha et al., 2010)

H10 – Influential members in LWP will have more connections with other influential

members than in IU (Anger & Kittl, 2011; Bakshy et al., 2011; Cha et al., 2010)

Phase Two:

Case Study Research Questions

R1: How do influential users interact with others?

R2: What online behaviours do influentials display that distinguishes them from

followers?

R3: What are the online behaviours that inspire their rise to power?

R4: How do influential individuals lose their power status?

R5: Exploring online identities of influentials

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3.5 Community Selection

The data consisted of forum posts from two online discussion groups. The sites

in question have been given pseudonyms for the purpose of this research. Virtual

communities were selected using the Google search phrase “vBulletin” and “ideology”

as these search terms produced online communities that included visible reputation

scores for each of the users within these discussion sites. This particular research needs

some verification that the users that are being examined are indeed influential to their

peers; consequently, only communities that used reputation scores and reputation

power were used for the study (see detailed discussion below). Ultimately, this led to

the selection of two online communities one left-wing community (referred to as Left-

Wing Politics; LWP) and one Islamic Community (referred to as Islam United; IU).

The names of these communities have been changed due to ethical reasons and no

identifiable data is given throughout the thesis. In addition, all of the online community

members have been given aliases and the only identifiable data will be the quotes that

are taken directly from each of the forums, to adhere with ethical guidelines regarding

re-identifiable information (BPS, 2018).

The first case study was a far left political discussion forum known as Left-

Wing Politics (LWP). The LWP community is an ideological left-wing forum.

Roughly, this forum has over 10,000 users and over 2 million posts yielded from a 10-

year archive. LWP has 32 sub-forums discussing literature, art and opposing

ideologies. This particular forum has thousands of members from a variety of

progressive tendencies (such as communist, anarchists and Marxist Leninists).

The second online community, Islamic United (IU), is a religious forum,

dedicated to concepts and practices central to the five pillars of Islam. IU has around

900 active users across 25 sub-forums and 500,000 posts from a 7 year achieve. Both

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online communities are founded on strong ideological beliefs (religion and politics) so

members are enthusiastically involved. This is particularly important as the various

public spaces and online resources means that individuals are free to search and find

forums significant to their beliefs which means that individuals can find public domains

where they are “comfortable” and can consolidate into groups with other like-minded

individuals. With regards to politically endorsed online fora numerous authors have

remarked at the “ideological purity” within these discussion forums and claimed there

is a strong tendency to agree or enforce opinions, rather than challenge them (Davis,

1999; Jansen & Koop, 2005; Sustein, 2001; Wilhelm, 2000). Consequently, the notion

of authority and influentials maintenance is prominent within many of the discussion

threads within the selected online communities as these forums are uniting individuals

with a common goal.

Furthermore, both IU and LWP implement vBulletin Software which provide a

measure of status via reputation rankings (see figure below).

Figure 3.1. Screenshots of user information and reputation scores.

“Reputation” and “reputation power” both indicate a community members’

potential status. Reputation is a way of monitoring the quality of a user’s posts via

reputation rankings (vBulletin, 2019). If the site administrators have enabled this

setting then the reputation icons will be visible to all community members in their posts.

They are there so that at a glance individuals can gauge how much credibility to give

authors of posts. Reputation scores are assigned visible scores, which illustrate

potential influence or popularity (Joinson & Dove, n.d.). They are assigned to each

65

user so peers can rate post positively, negatively or neutral. Essentially, each user’s

ability to affect one-an-others’ reputation is determined by the magnitude of their own

reputation points score known as “reputation power”. That is, if those with a high

“reputation power” rate another member positively, the subsequent members’

reputation ranking will increase. Overall, “reputation power” is the product of; total

number of days in a community, number of posts and the user’s own reputation score.

Alternatively, an individual’s “reputation” is the number of points accumulated

for all of the posts that have received reputation, for example a user might receive 1

reputation bar for every 100 reputation points that are given by other users. To put it

bluntly, “reputation” is the number of points one can give or take away when reputation

is given to a post. Previous research has identified some methods for examining the

development of authority and influence in online communities (Joinson & Dove, n.d.).

Generally, reputation and reputation power appear to be relatively good indicators of

status and influence within a community; however, it must be noted that it is just one

of aspect of the meta-data that was collected from these communities. Those with a

high reputation power scores regularly post comments that are rated highly by other

members and have long-standing membership in an online community.

Based on this visible information, eight users from each of two online

communities were selected for further analysis (16 in total) as they were deemed to be

those with some level of influence within the communities. Accordingly, all the threads

started by these selected individuals were reviewed. A thread is an initial posting with

subsequent responses; a post is a reply to the thread discussion. See figure below.

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Figure 3.2 Example of treated discussion, replies and self replies. Adapted from

“Finding influential users of online health communities” by K. Zhao, J. Yen, G. Greer,

B. Qui, P. Mitra, & K. Portier (2014), Journal of the American Medical Informatics

Association, 21(e21), e212-e218.

Threads and the thread originators, opposed to individual posts, were chosen for

analysis specifically as these are comments that user’s themselves have initiated rather

than followed which displayed a more coherent timeline. Equally, this is congruent

with existing research as threads “reflect each user’s potential to influence others’

sentiment” (Zhao et al., 2013, p.213). If posts, opposed to threads were chosen for the

analysis, the researcher would not necessarily have seen the context of the conversation

and other posts (from other users) may have been missing. Nonetheless, some posts

were also visually reviewed to ascertain the applicability of the research findings.

3.6 User Selection

Eight users were selected from the discussion forum Left-Wing Politics (LWP)

for the analysis and a further eight from Islamic United (IU). These users were

determined via social network and behavioural marker analysis to have had influential

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status in the community (at some point) based on the meta-data collected. This meta-

data included the measure of “grounded” reputation via reputation rankings as well as

linguistic, semantic and social network analysis. In other words, the reputation data

collected could also help track the networks between community members. Therefore,

this meta-data helped to identify specific roles within the community based on patterns

of behavior (frequency of posting, thread starting, question asking) and position within

networks, which helped trace roles and status development within the communities.

Accordingly, an algorithm identified those most likely to be an influential in this

ideological forum based on communication skills, activity levels and linguistics style.

Based on the behavioural markers, the algorithm grouped users into one of four

categories; leader, collaborator, contributor or inactive user/reader.

These grouping criteria are based on Preece and Shneiderman’s (2009) reader-

to-leader framework. This framework describes how often people join social media by

first reading, then briefly contributing (asking a question or uploading a photograph).

Then, some users go onto collaborate with other users by making a video or even co-

writing an article together, or some users adopt an authoritative role by helping

newcomers to undergo a similar behavioural pattern and maintain community policies

and norms (Preece & Shneiderman, 2009). See the figure below.

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Figure 3.3 Reader-to-Leader Framework by J. Preece, & B. Shneiderman (2009), AIS

Transactions on Human-Computer Interaction, 1(1), 13-32.

The above figure is a framework which categorises online community members

based on roles, while this framework discusses potential ‘leaders’, it is used in the

present research to discuss those individuals whom are influential within the online

community. The reader-to-leader framework defines a reader/inactive user as the

typical first step towards more active participation (Kollock, 1999). This is used to

describe someone who is not making a visible contribution but maybe reading forum

content. Alternatively, a contributor is when an individual adds a larger communal

effort; these can be modest steps such as making a correction or even rating a film; thus,

there is no intention of collaborating or forming bond with others (Preece &

Shneiderman, 2009). Specifically, with LWP this was an individual who was a low

volume supporter, meaning they may have only provided a small amount of information

or asked questions when new to the forum. Collaboration involves two or more

contributions, however modest, though there must be a common ground established in

this step. That is, there must be mutual understanding between users to facilitate their

communication (Preece & Shneiderman, 2009). Therefore, these individuals were

deemed to be conversationalists in the online forum.

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While individual contributions and group collaborations are the most visible in

social media, there still needs to be a way of establishing community norms (Burke &

Kraut, 2008), this is the role of a “leader” (or influential). As such, “leaders” tend to

synthesise communications and discussions to articulate to others and recognise their

respected role in a community (Kim, 2000). Consequently, within online communities

these “leaders” were most likely to be popular participants (classed as influentials for

the purpose of this research). Therefore, eight popular participants were chosen from

each community for the analysis as they had all become “leaders” or reputable

individuals at some point in their forum life-span. However, there was a diverse range

and combination of different reader-to-leader steps. Also, it should be noted that

individuals may also terminate their participation and become inactive for a number of

different reasons which are not represented in the above figure.

3.7 Data Collection for Study One

The data collection for phase one of the analysis consisted of a technique called

“screen scrapping”. In other words, this is a way of conducting large-scale cut-and-

pasting of webpages. Scraping was conducted using the MySQL tool; this is an open-

source relational database management system which large volumes of data can be

sorted and accessed upon certain commands. No scraping behind logins was attempted

only publicly available information was stored. All posts from both LWP and IU online

communities were scraped and subsequently stored using the MySQL. MySQL

database was chosen as it is the world’s second most frequently used open-source

relational database management system (RDBMS). For LWP this generated a dataset

of 1,494,464 posts from 11,778 users dating from July 2001 to November 2011.

Alternatively, for the online community IU the dataset consisted of 485,299 posts from

3,205 users ranging from April 2004 to December 2011. All these posts were stored in

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MySQL alongside identifiable information such as poster name, posting place, number

of thanks received, those that were re-quoted (and who by), and word count.

3.7.1. Sampling

The sample technique was derived from the previous literature on identifying

influentials from Joinson and Dove (n.d.). Though, some modifications were made, for

the purpose of phase one, 4-status groups were created in the database as only

individuals that were at some point in time deemed influential were of interest for this

aspect of the research. These individuals were chosen from the database as this was the

best way to gauge reputation, as the information in the database was only a “snapshot”

at the current time of scraping. In other words, reputation cannot be traced or tracked

in the database and only reputation at the time of collecting the posts was stored and

cannot be a reliable estimate with regards to social roles and transitions. The following

query was used to identify those from the database that were always influential/popular

throughout the two year cluster analysis;

SELECT * FROM roles_timeliced. rl_roles_timesliced

WHERE

24_18_cluster = 'Cluster 0' AND 18_12_cluster = 'Cluster 0'

AND 12_6_cluster = 'Cluster 0' AND 6_0_cluster = 'Cluster 0'

Figure 3.4. The Query Used to Determine Users from MySQL Database

Therefore, those individuals that had always been influential over a two-year

cluster analysis were included in the sample. Equally, influential individuals who

became inactive or left the community were included, influentials who gradually lost

their status and went to a lesser role were included as well as those individuals who

gradually gained status (went from collaborator/contributor to popular) within the

community were included to compare online behaviour in conjuncture with the

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hypotheses. The following query was used to update a column and create a group

identifier;

UPDATE rev_left.all_revleft3

SET `status` = 1

WHERE

poster like '%blackmagi%'

Figure 3.5. The Query Used to Create Table from MySQL Database

This query enabled MySQL to select specific poster identifiers and give them a status

number (status 1, 2, 3 or 4), which was dependent upon their social role.

Table 3.3

Showing the groups used in Phase Two from LWP and IU

Status Group Group IU

N

LWP

N

1 Always

Influential/Popular

12 43

2 Influential – Demoted 10 29

3 Influential – Inactive 5 19

4 Promoted to Influential 9 17

3.7.2. Selection Criteria

With regards to those that were promoted to influential status and those that

were demoted there was strict criteria in the database for the selection process. Only

3 Coding scheme used to general database information

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individual users that had been influential for a year out of the two-year cluster analysis

were included in the in the demoted category (group 3). Similarly, only those that had

been influential for the last 12 months of the cluster analysis were able to be included

in the promoted category. Therefore, the status groups were clearly defined to allow

comparisons on posting/thread behaviour, number of thanks and word count.

3.7.3. Analysis

Analysis of the relationship between the IV (status) and the dependent variables

(seniority [total number of posts], post word count, reputation, and number of thanks,

average number of thanks, thread total and thread word count) were analysed using a

MANOVA. A MANOVA was conducted opposed to a series of univariate analysis of

variance (or ANOVA) to reduce the likelihood of making a Type I error (rejecting null

hypothesis when it is true). As there were multiple hypotheses in the present

investigation the chance of making such an error was significantly increased; thus,

validating the choice of multivariate analysis of variance, opposed to ANOVA.

Equally, this analysis takes into account the possibility of intercorrelations amongst the

various dependent variables (Langdridge, 2005).

In addition, social network analysis (SNA) was conducted to assess socially

meaningful relationships. In that, an analyst of social networks is able to examine the

ties of individuals to other members within a network and assess how individuals are

positioned/centralised in overall network patterns (Prell, Hubacek, & Reed, 2009).

SNA is the mapping of relationships or informal connections between individuals

within groups or organisation (Ehrlich & Carboni, 2005). In order to understand a

particular individual or network SNA examines the location of a node; by locating or

measuring a particular node, one must assess its centrality. In other words, is this

particular node in the core of a network or in the periphery? And how well connected

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is that node to reach others in a particular organisation? The graphs below used the

following graph metrics; betweenness centrality, eigenvector centrality (closely related

to degree centrality) and closeness centrality to create visual network graphs to reveal

centrality. However, as eigenvector and closeness centrality are considerably smaller

than betweenness centrality in size smaller and therefore do not show dramatic

differences, they were not reported in the tables below.

The graphs are built up of nodes and edges that connect them. Each circle is a

VC user and the connection is a tie or social relationship. Thus, that illustrates an

exchange in communication. The more central an individual is within a network, the

more access individuals have to other constituents. Equally, if an individual is

connected to a popular neighbour, then that specific relationship or edge would produce

a high level of potential influence of diffusion. See diagram below for basic principles

of social network graphs.

Figure 3.6. Diagram showing the various nodes and edges in a social network analysis.

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Consequently, social network graphs are based on the cluster analysis, which

was time-sliced and divided into four (24-18 months, 18-12 months, 12-6, months and

6-0 months) from the two-year period. These diagrams are used to illustrate the change

in social structure from influential statuses to other social roles and clearly portray

social ties and connections with LWP and IU (See Appendix 4).

3.8 Data Collection for Study Two

3.8.1.Design

The second phase of the research adopted qualitative methods to investigate the

social processes that occur online. In phase two narrative analysis was used to explore

this lived phenomenon in the naturalistic context in which it occurred. A wide range

of disciplines find narrative analysis and story-telling processes useful in explaining a

variety of social and psychosocial processes (Mankowski & Pappaport, 2000).

Researchers of online communities have favoured this qualitative approach as

narratives themselves appear to serve a variety of functions in spiritually based

communities (such as membership and identity development; Kozinets, de Valck,

Wojnicki, & Willner, 2010). Accordingly, research on narratives may be useful in

examining individual level experiences, community building, communal norms and

social processes.

Van De Mieroop (2015) states that are social groups are fluid and inherently

evolve over time; therefore, language occupies a central role in creating identities.

Furthermore, “identity is thus viewed as a discursive accomplishment and a product of

social interaction” (Van De Mieroop, 2015, p.410). Accordingly, narrative analysis of

individual’s social groups or categories, best embodies the fluidity and changes over

time as individuals can shift in and out of social groups which can be best examined

through narratives of collective identities (Holmes, 2006; Labov, 2006; Smith &

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Sparkes, 2008). Additionally, narrative analysis can focus on which memberships are

made relevant by the individuals in the investigation and how their membership serves

a specific function to their social identity (Holmes, 2006).

Furthermore, this can allow the researcher to follow and document clearly

participants “down their trails” (Riessman, 2008, p.24). Therefore, two online

communities were selected as cases for “an in-depth, intensive and sharply focused

exploration” of behaviour (Willig, 2010, p. 74).

3.8.2. Sample

Influentials were identified from the dataset based on their reputation and

reputation power scores. Those that were deemed to be influentials had a reputation

and reputation power score within the top 10% of all the users in the entire community.

In LWP the top 10% of users resulted in a sample of 353 leaders, eight of which were

used for the analysis. In the IU community, those ranking in the top 10% produced a

sample of 245 leaders, which were extracted for qualitative analysis. These users were

of interest in the research (see below for detailed description of individual user

selection).

3.8.2.1. Four Users that Rose to Popular Status in LWP:

The first user “Black Magician” first joined LWP in July 2005. On average,

this user posts twice daily. “Black Magician” has a total of 6,289 posts and 121 threads

started. This community member has been thanked over 8,000 times by different forum

members. Based on the computational linguistics, it is likely that “Black Magician”

has had long-term status within this discussion site and has been an avid member of the

forum based on an observation of online behaviour. This users’ last activity was on in

May 2013. Therefore, was still active within the community at the time of data

collection.

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Second, “Guy Fawkes” first joined this discussion site in March 2006. This

user has 2,869 total posts and 203 threads started. On average this user posts once per

day. “Guy Fawkes” has been thanked over 2,000 times and was last active in the forum

in September 2013, thus was still engaged in the community at the time of data

collection. On a behaviour note, this user has hidden his reputation score from others.

The algorithm categorised this user as initially a contributor, then collaborator and

finally an influential. As such, this user has engaged in a variety of reader-to-leader

steps (Preece & Shneiderman, 2009).

Third, “Community Member” has a total of 1,000 posts and only 7 threads; on

average, this user posts less than one post per day but has been thanked by other

members over 800 times. “Community Member” joined LWP in June 2009 and was

last active in the community in June 2012. Therefore, it is likely that this user is now

inactive. Based on the computational techniques over a two year period this user has

gone from contributor to collaborator, back to contributor and finally a “leader”.

Finally, “Guerrilla Warfare” first joined the community in June 2004 and has a

total of 3,668 posts and 189 threads. On average, “Guerrilla Warfare” posts once daily

and has been thanked over 700 times by other forum members. Based on the

information received via computational linguistics, this member went from “leader” to

inactive. Their last activity was in September 2010. This member has been banned

from the forum. All of the four users are presumed male based on the information and

content of their posts and are referred to as male by other users.

3.8.2.2. Four Users that Rose to Popular Status in IU:

The first user “Gag Order” joined the forum in August 2004. This community

member has received 82 likes from others. Moreover, Gag Order has been thanked

1,825 times in 869 posts but has not been active in the community since August 2011.

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Gag Order has a total of 5,289 posts and 44 threads started. Based on the cluster

analysis, Gag Order joined the forum and became a collaborator then moved to “leader”

or popular participant status. There is no mention of the gender of this users’ profile

but based on the posts and correspondence between various other IU members, it is

presumed Gag Order is male due to the frequent reference “brother” from other

members.

Secondly, “Intoodeep” joined IU in June 2006 and still remains active at the

time of data collection. Intoodeep has only given one like but has received ten.

Additionally, this user has been thanked 299 in 135 posts. Overall, Intoodeep has 1,749

posts and 54 threads. Based on the analysis, Intoodeep jumped from contributor to

“leader” status throughout his time in the community. While there is no mention of

gender, this user is also presumed male based on correspondence with other IU

members. “Intoodeep” is classified as a “Senior Member”.

Third, “Pluma” joined in October 2008 and was still involved in the community

at the time of data collection. Pluma has received ten likes and has been thanked 559

in 302 posts. Overall, Pluma has posted 2,977 times and has initiated only 22 threads

(though has received many replies from others). Pluma (like Gag Order) moved from

collaborator to popular status throughout his time in the community. While there is no

mention of gender, other members frequently refer to Pluma as “brother”; thus, it is

presumed Pluma is male.

Fourth, Al-Omari joined in June 2004 and last contributed to the community a

week ago (1/1/2014). Therefore, this member was still active at the time of data

collection. Al-Omari has received 15 likes, 1,942 posts and 49 threads started (with a

large amount of replies). Al-Omari speaks Arabic and is categorised as a “Senior

Member”. Overall, Al-Omari has been thanked 556 times in 319 posts. Al-Omari was

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a contributor for a large amount of time, until recently when he became a ‘leader’. Once

again, there is no mention of gender, but is referred to as “brother” by other members’.

3.8.2.3. Four Users that Lost Status in LWP

Following on from the above section, four users were selected from LWP

forum. However, opposed to examining individuals that became influential this aspect

selected individuals that the previous analysis has identified that they transitioned role

from influential status to a lesser role within the community. As such, four users that

lost status were as follows:

“Brothering” first joined LWP in January 2008. This user has a total of 1,483

posts and 166 threads started. This LWP community member has been thanked over

300 times by different members and was last active 2 years ago. Based on the

information Brothering has provided it states gender as both Male and Female.

However, based on the username selected it is presumed that Brothering is male.

Brothering went from contributor, to “leader” and then to a collaborator.

“Kay” joined the forum in October 2008 and is still an active member of the

community. Overall, Kay has over 2,000 posts and has started over 140 threads. This

user has been thanked 2,453 times and has stated his gender as male. This online

member moved roles contiguously from “leader” to collaborator then back to “leader”

and down a role transition to collaborator once again.

“Skinhead” has a total of 1,610 post but 61 threads and has only been thanked

694 times by other LWP members. While there is no mention of gender in this user’s

profile, he is presumed male on inspection of his threads and posts. This user joined

the forum in June 2005 and was last active in October 2013. This online member went

from collaborator, to contributor, to “leader”, then collaborator.

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Finally, “Comrade Believer” first joined the forum in May 2008 and was last

active in October 2013. This user has over 1,000 posts and has started 186 threads but

has only been thanked by other users 209 times. This user has claimed to be male in

his posts and threads. This online community member was a contributor for a long

period of time then progressed to “leader”.

3.8.2.4. Four Users that Lost Status in IU:

The first user “Umar247” first joined Islamic United in February 2008. This

user has received a total of ten likes from other members within the online community.

Overall, “Umar247” has a total of 1,407 posts and has started 143 threads. This user

has also been thanked 233 times in 100 posts/threads from other community users (but

given none) and was still an active member of the community at the time of data

collection. Based on the computer linguistics, “Umar247” entered the community and

became an influential member or “leader” relatively quickly. His activity level then

declined and he was categorised as a contributor but then rose up through the ranks

again and became a collaborator. This user has stated their gender as male in his profile.

Second, “Free the Oppressed” first joined IU in December 2009. This user is

presumed female through her interactions with others. She has 935 posts throughout

her time in the online community and a total of 111 threads started. She has been

thanked 15 times in 10 posts but not given any likes to other members. On average,

she posts once per day and is currently categorised as a “Senior Member” and was still

active within the community at the time of data collection. Based on the statistics,

“Free the Oppressed” became a “leader”, and then subsequently went inactive. Her last

post was in October 2010.

Community member “Iqra” first joined the community in November 2006. Iqra

is also presumed female due to her various encounters with other members where she

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is referred to as “sister”. On average, she posts less than once per day but has a total of

508 posts and 57 threads. She has been thanked 3 times in 3 posts. Computer analyses

categorise her as a contributor, then a “leader”, then inactive. She has been inactive

within the community since March 2010.

Finally, “Weshallnotkeepsilent” first joined IU in March 2010 but is still

categorised as an “Active Senior Member.” This member’s last post was in October

2013. It is presumed due to behavioural characteristics and interactions that

“Weshallnotkeepsilent” is male. This member has a total of 914 posts and has started

52 threads, on average he posts approximately once per day and has been thanked 263

times in 168 posts by other IU online community members. It is presumed based on

the computational analysis that Weshallnotkeepsilent was a “leader”, then slowly

became a collaborator.

3.8.3. Analysis

The analysis was conducted through narrative analysis. This method uses

documents or interviews to follow participants’ trails (Riessman, 2008). This analytic

procedure refers to a large number of approaches to various different types of text

(Riessman, 2008). Narrative analysis seeks to put together the ‘big picture’ about

experiences or events as participants understand them. Equally, this process aims to

understand both the sequence and the consequence of occurrences. Narrative analysis

focuses on the story itself and attempts to preserve the integrity of particular events as

they happen by displaying the goals and intentions of human actors (Richardson, 1995;

Riessman, 2008). Indeed, this method is “based on the belief that we interpret and

construct the world around us through interactive talk” (p. 90, Wiles, Rosenberg, &

Kearns, 2005).

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There are a variety of different approaches that fall under the term ‘narrative

analysis.’ Some of these approaches can be stringent in the way in which data is

organised and some favour more interpretative strategies employed by researchers

(Wiles et al., 2005). However, this research incorporated a thematic analytic procedure

to narrative analysis in that there was a clear framework to devise and recognise patterns

within the entire data set (Braun & Clarke, 2006). Therefore, this research employed

Reissmans’ (2008) methodological approach to narrative analysis.

Riessman (2008) claims that there are four different methodological approaches

to narrative analysis (see table below). Thematic analysis was the appropriate analytic

strategy for this thesis.

Table 3.4

Showing the four different approaches to narrative analysis.

Model of Narrative Analysis Rationale of Process

Thematic Analysis Emphasis on what is told, and the content

rather than specifically how it is told.

Structural Analysis Language plays a stronger role in this

approach. Focus on how language is

used to tell the story.

Interactional Analysis Stories of personal experience are

organized around the teller. Also

emphasis is placed on the teller and

listener and the dialogic process.

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Performative Analysis Storytelling is seen as a performance and

goes beyond the spoken word.

Note: Different models of narrative analysis. Adapted from “Narrative

Methods” by C., K. Riessman, 2008, “Narrative Methods for the Human Science.”

Thousand Oaks, CA: Sage.

Each of the various approaches in the table above implies a different attitude

towards the data. This, in turn, reflects upon the epistemological assumptions entwined

in the data collection phase and ultimately impacts upon the role that the transcripts

play in the research. For this research, a thematic approach to narrative analysis was

adopted as it focuses on the content of the narratives as well as how the narrative is

written. This approach is often found in health journals as a way of analysing large

amounts of text from online discussion boards (Kannaley, Mehta, Yelton, & Friedman,

2019).

As such, the main emphasis is on what was said by individual community

members, rather than how it was said (Riessman, 2008). While language is not the sole

feature of the investigation, it is still an integral feature (or resource) of the analysis. In

further justification to the thematic narrative analysis, it is also particularly useful for

finding and grouping themes across a number of cases (Riessman, 2008). (Birch, 2011)

The coding for narrative analysis involves coding the narrative as a whole,

rather than selecting specific aspects and grouping them into general patterns.

Riessman’s (2008) thematic approach to narrative analysis relies on categorising

accounts or aspects of an account told by an individual. This approach to the data

aided the researcher in moving from simply reading the data set to discovering

emergent patterns in the data. Braun and Clarke (2006) claim there is a lot of overlap

between thematic analysis and narrative analysis as they “share a search for patterns

and themes across and (entire) data set” (p. 8).

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As there is no standardised method for assessing broad patterns emerging in

data, a thematic framework was incorporated into the process. This method provided

“a flexible and useful research tool, which can potentially provide a rich, detailed, yet

complex account of data” (Braun & Clarke, 2006, p.78). Generating themes from data

is a common analytic method in qualitative research methods. As such, thematic

analysis is the interpretative process where the data is systematically searched for

reoccurring patterns to provide a detailed description of the phenomena under

investigation (Tesch, 1990).

This process involved reading the entire data set repeatedly and noting down

any initial ideas. These ideas are then coded based on interesting features of the data

for potential themes. The entire data set was then searched for repeated patterns of

meaning or themes. Once the reoccurring themes were established they were grouped

together, allocated clear definitions and divided into various subthemes accordingly

(Braun & Clarke, 2006).

As such, there were 1,072 threads in the corpus for LWP forum and a further

532 threads from IU for the 16 online community members (with relevant replies from

users). Braun and Clarke’s (2006) framework was utilised in this process.

Accordingly, the data corpus was reviewed and any noteworthy or interesting features

were then taken from the data set. Overall, the data corpus was searched and any

interesting aspect relating to social identities in line with the theoretical framework

(Tajfel & Turner, 1979). As such, any interesting aspect of online identities or group

categorisation was noted.

Subsequently, in line with Braun and Clarke’s (2006) framework for thematic

analysis, a content analysis (tally chart) illustrates the most prominent themes and

subthemes. This content analysis procedure was undertaken to determine the most

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prominent themes/subthemes from each of the community member and these were the

discussed themes in the analysis. This process adapted from the work of Macià and

Garcia (2016) and their procedure for focusing large data in online community research

and generating themes. Equally, due to the large scale nature of the data corpus this

was the most logical way to assure that the most prominent themes and subthemes were

extracted for the final themes for the analysis.

Whilst this does seem an usual method for qualitative enquiry, Gbrich (2007)

does claim that it is indeed possible for qualitative analysis whilst quantifying the data

(though this seems like a juxtaposition). Moreover, content analysis in this example is

utilised as a method for clarifying the data via quantitative counts of the codes and is

merely one of the steps taken in the thematic analysis (Downe-Wamboldt, 1992;

Morgan, 1993).

3.8.4.Triangulation

To demonstrate qualitative rigor various strategies were incorporated into the

research design. One way to establish credibility throughout the research was to

incorporate methods triangulation (Patton, 1999). As such, prolonged exposure to the

various online communities ensured that the researcher is interpreting the textual

information correctly. Equally, having observed interactions with other users, it

allowed for a more detailed analysis and greater understanding of the topic being

discussed as the researcher gained a better understanding of the conversation style of

each user and their friendship-ties with other forum members. This essentially helped

understand the context of forum posts.

Dependability was established via a clear audit trial. Therefore, adopting

systematic steps in the analytic process enhanced the trustworthiness of the data.

Ultimately, this permits external checks and validations through the documentation of

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the analysis procedure (Drissen, van der Vleuten, Schuwirth, van Tarwijk, & Vermunt,

2008). Finally, a reflexive account was also incorporated to facilitate any preconceived

ideas the researcher may have previously held before the analysis any issues that the

researcher may have overcame.

3.9 Ethical Considerations

With regards to ethical considerations, firstly, the necessary procedural ethical

clearance was granted from the University (See Appendix 1). In addition to the latter

point, all identifiable information was removed from the data and only quotations were

included in the analysis to support analytical claims (Coolican, 2009; Roberts, 2015).

Utilising online communities as a way of collecting qualitative data creates unique

ethical challenges; such as the ‘traceability’ of quotes (Beaulieu & Estalella, 2012).

To tackle such issues, quotations were anonymised and in some cases, paraphrased to

avoid further trace.4

Online community members were given pseudonyms to conceal their online

community identities (usernames) and to provide confidentiality (Roberts, 2015).

Equally, in line with research strategies to improve ethical research, the online

communities themselves were also given pseudonyms and website addresses have not

been disclosed (Malik & Coulson, 2013). Therefore, only the researcher knew the

identities of the online community members that were included in this research

(Lahman et al., 2015).

A further ethical complication around the use of internet data, specifically

online communities is the acceptability of covert research. As such, there is much

4 it should be noted that these online communities have since been closed down due

to some of their ideologies and are not traceable via search engines

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debate around whether researchers should identify themselves as researchers and

whether this is classified as deception (Reir, 2007; Roberts, 2015). In the case of this

research, covert, passive research was conducted (Reir, 2007). In other words, the

researcher did not identify as a researcher whilst observing the online community (and

creating a username to do so) and the researcher did not make any contributions such

as posts or threads during their time in the community. Therefore, this passive approach

“did not intrude on participants or group discussions” nor deceived other online

community members about their intentions or identity (Roberts, 2015, p.12).

Whilst the internet does undoubtedly provide a way to collect data from diverse

participants and explore new social phenomenon, it is not without ethical challenges

(Kraut, Olson, Banaji, Bruckman, Cohen, & Couper, 2004; Roberts, 2015). Another

issue surrounding the ethics of online community research is the notion of informed

consent. It is generally accepted that research should obtain the consent of participants;

however, passive data collection methods promote a difficulty in seeking and acquiring

such consent from participants. As such, this online community setting was

acknowledged by members as being a ‘public domain’ no further permissions were

sought as this is seen as public data; thus, no consent is needed (Honey & Herring,

2009; Kitchin, 2003; Roberts, 2015; Sigura, Wiles, & Pope, 2017).

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CHAPTER FOUR

STUDY ONE: QUANTITATIVE ANALYSIS OF ROLE TRANSITIONS IN

ONLINE COMMUNITY

4.1. Overview of Chapter

The following chapter illustrates the individual data and online beahvioural

patterns for a number of online community users. Overall, this involved a detailed

display of posting and thread starting behaviour and this was found to be a reoccuring

pattern of behaviour as previous research posited these characteristics were significant

factors of influential individuals. However, number of thanks was not included in this

section as there was no data before 2009, indicating this was a relatively recent feature

added to the communities. Social network analysis (SNA) demonstrated popularity and

centrality within the online community and social role transitions based on the cluster

analysis in the table below which were referred to throughout the previous chapter.

As well as detailed examination of 16 users that exhibit strong role transition,

this sections also examined all those that rose to status (and lost status) for both VCs

LWP and IU. More specifically, this involved seperating all those that rose to status

(at some point in between 2009-2011) and categorising them into four groups.

Subsequently, detailed statistics examining the four categorised groups were analysed

to investigate the hypotheses. Finally, this chapter examined potential differences

between the two communities to ensure that levels of influence were similar between

IU and LWP.

4.3. Left Wing Politics Analysis

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This section will portray behaviour characteristics for each of the eight users

discussed in phase one for LWP.

Table 4.1.

Showing role transitions for eight users of LWP over a 24-month period

Months

LWP Member 24 – 18 18 - 12 12 - 6 6 – 0

Members that Gained Reputation (Part One)

Black Magician Leader Leader Leader Leader

Guy Fawkes Contributor Collaborator Leader Leader

Community

Member

Contributor Collaborator Contributor Leader

Guerrilla Warfare Leader Collaborator Inactive Inactive

Members that Lost Reputation (Part Two)

Kay Leader Collaborator Leader Collaborator

ComradeBeliever Leader Contributor Contributor Contributor

Skinhead Collaborator Contributor Leader Collaborator

Brothering Contributor Contributor Leader Collaborator

4.4 Summary of LWP members and Hypothesis Development

As illustrated in the charts in the appendices, when individual members are

gaining status (or indeed holding status), there is an increase in posting, thread starting

behaviour and word count which appears to correspond with the cluster analysis in the

table at the beginning of the chapter. Conversely, when members are losing their status,

there is a decrease in activity. Whist this does not state a cause and effect, it does

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illustrate that there are certain online behaviours which parallel perceived status. Many

of the charts for individuals who gain status show a gradual increase (with the

occasional exception) and many of the charts in the losing status part of this chapter do

illustrate a slope or decline in thread starting and posting behaviours. Consequently,

this appears to add some theoretical implications for the hypotheses, in that, posting

and thread-starting behaviour (in particular) do appear to be associated with influential

status. The table below lists the hypotheses relevant to this chapter.

4. 2. Summary of Hypotheses:

Table 4.2

Hypotheses for Phase One

Phase One:

SQL

H1 – Influential individuals will have a higher number of posts in comparison with

their counterparts

H2 – Influential individuals will have a higher number of threads in comparison with

their counterparts

H3 – Influential individuals will have a higher word count (per post) than their

counterparts

H4 – Influential individuals will have a higher word count (per thread) than their

counterparts

H5 – Influential individuals will have a higher number of thanks from other

community members than their counterparts

H6 – There will be a relationship between posting frequency and status

H7 – There will be a relationship between thread starting frequency and status

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H8 – There will be a relationship between acknowledgement from others and status

H9 – Influential members of IU will have more connections to other members in

comparison with influential members of LWP

H10 – Influential members in LWP will have more connections with other influential

members than in IU.

4.5. SNA for Online Community Members

Overall, it is evident from the social network graphs in appendices that online

community members tended to demonstrate far more centrality within the network

when they were holding an influential status within the online community. While some

individuals were far more central than others, all of the diagrams in the appendices

show a high level of centrality when users were categorised as having an influential

status within the community. Equally, when individuals were categorised as

influential/popular they tended to have more ties to nodes and edges within the social

structure, compared to other social roles.

In relation to the hypotheses, influential individuals tended to have more edges

with others who were also placed relatively central within the social structure. These

individuals are also likely to have increased influence and betweenness; thus,

demonstrating a high level of social capital. In many of the graphs (See Appendix 4),

users tended to have a higher number of short edges when they were occupying

influential status within the two online communities. These short ties represent

betweenness centrality and high social status. PageRank also appears to increase with

status, suggesting that they form more ties with other influential individuals when

gaining high-ranking status.

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4.6 Global LWP Descriptive and Inferential Statistics

This section examines all of the individuals that gained and lost status within

LWP over a two-year period. In expanding from the previous eight users, this section

includes N = 108 LWP online community members, which have been influential at

some point within the cluster analysis and relates to the following hypotheses:

H1 – Influential individuals will have a higher number of posts in comparison with

their counterparts

H2 – Influential individuals will have a higher number of threads in comparison

with their counterparts

H3 – Influential individuals will have a higher word count (per post) than their

counterparts

H4 – Influential individuals will have a higher word count (per thread) than their

counterparts

H5 – Influential individuals will have a higher number of thanks from other

community members than their counterparts

The independent variable in the present investigation is the group status that has

four levels (see table below). Group status is a means of assessing reputation over time;

therefore, the levels are as follows; always influential, influential then demoted to

another role, influential then inactive and promoted from a lesser role to influential.

Equally, there are a number of dependent variables which are as follows; reputation,

post total, post word count, thread total, thread word count, average word count per

posts, average word count per thread, number of thanks and average number of thanks.

As previously mentioned, reputation is not a reliable indicator of status as this was taken

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as a snapshot when the community was meta-scraped; however, this is still included as

a dependent variable in the following section to further verify and support the different

status groups as a way of assessing social capital. As such, those who have always been

influential should have a higher reputation score than the other groups.

Table 4.3.

Group Properties for the Independent Variable Status

Group Identifier Number Status Group No. of participants (n)

1 Always Influential 43

2 Influential to Another

Role

29

3 Influential to Inactive 19

4 Promoted to Influential 17

Descriptive Statistics

Table 4.4.

Group Status Mean and Standard Deviation Scores for Dependent Variable

Reputation

Group Identifier

Number

Mean SD

1 3705.33 2369.196

2 1032.72 831.390

3 767.58 2714.287

4 527.94 530.666

Table 4.5

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Group Status Mean and Standard Deviation Scores for Dependent Variable Post

Total and Post Word Count

Post Total Post Word Count

Group Identifier

Number

Mean SD Mean SD

1 4077.40 2400.971 249215.93 222554.996

2 2088.24 2656.973 105.9650 93926.351

3 3800.58 5530.725 134.2905 366635.303

4 3800.58 2596.211 97.3186 130105.482

Table 4.6.

Group Status Mean and Standard Deviation Scores for Dependent Variables Thread

Total and Thread Word Count

Thread Total Thread Word Count

Group Identifier

Number

Mean SD Mean SD

1 60.79 66.206 12369.81 13413.554

2 24.90 27.817 5714.55 8145.960

3 49.63 60.390 14464,89 22003.684

4 26.94 36.450 6407.06 7920.250

Table 4.7.

Group Status Mean and Standard Deviation Scores for Dependent Variables Average

Thread Word Count and Average Post Word Count

Average Thread Word Count Average Post Word Count

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Group Identifier

Number

Mean SD Mean SD

1 252.1730 269.97332 62.6250 42.98549

2 345.5033 854.85891 55.4558 35.84885

3 277.4852 236.35806 51.5646 28.10025

4 313.9647 522.35140 55.6137 54.84119

Table 4.8.

Group Status Mean and Standard Deviation Scores for Dependent Variable Number

of Thanks Received and Average Number of Thanks

Number of Thanks Average Number of Thanks

Group Status

Number

Mean SD Mean SD

1 1468.56 1148.123 3.9824 3.44720

2 324.10 285.366 10.1248 18.33318

3 468.89 531.710 22.3714 44.01906

4 440.35 310.930 11.2603 23.30610

Note. All missing values (-1) were excluded from number of thanks.

4.6.1. Inferential Statistics

4.6.1.1. Method of Analysis

Analysis of the relationship between the IV (status) and the dependent variables

(seniority [total number of posts], post word count, reputation, and number of thanks,

average number of thanks, thread total and thread word count) were analysed using a

MANOVA. With regards to the various assumptions that need to be met, Box’s M was

95

executed to determine the equality of variance covariance as well as Levene’s test for

homogeneity to assess equality of variance. In this case both the Box’s M result

(p<0.001) and Levene’s test were found to be statistically significant (p<0.05) for all

dependent variables apart from average word count (for both threads and posts).

Consequently, the assumption of equality amongst the various groups had been

violated. Despite ANOVA being relatively robust, non-parametric tests (Kruskal-

Wallis) were used to confirm the effects of the MANOVA, all apart from average word

count for posts and average word count for threads were significant at the p<0.05 level

(and seniority, word count for posts, reputation, average number of thanks and number

of thanks were found to be significant at the p<0.001 level). In every instance Kruskal-

Wallis confirmed the findings from the MANOVA. For the post-hoc comparisons

Games-Howell was used due to the uncertainty regarding equal population variance;

thus equal variance could not be assumed.

4.6.1.2. Multivariate Effects

A one-way MANOVA revealed a significant multivariate main effect for status,

F (27, 278) = 3.366, p <. 0001; Wilks’ λ = .438, partial η2= .241. Power to detect the

effect was 1.00. The MANOVA also found a significant intercept, F(9, 95)=51.621,

p< .001; Wilks’ λ = .170, partial η2= .830. Therefore, this demonstrates that there is a

difference in the means for status groups with regards to the various variables,

illustrating a different pattern of behavior for each of the four groups in the IV.

Significant univariate effects were found for all of the dependent variables

(apart from average word count per post and average word count per thread) at the

p<0.05 level or above. Seniority, F (3, 103) = 5.343, p<0.05, η2 = .075, power = .658;

Word Count for Posts F (3, 103) = 3.606, p<0.05, η2 = .095, power = .780; Reputation

F (3, 103) = 18.910, p<0.001, η2 = .355, power = 1.00; and Number of Thanks, F(3,

96

103) = 15.913, p<0.001, η2 =.317, power =1.00; Thread Total F(3, 103) =3.296, p<0.05,

η2 =.088, power = .738; Thread Word Count F(3, 103) = 2.722, p<0.05, η2 =.073, power

= .646; Average Number of Thanks, F (3, 103) = 2.913, p<0.05, η2 = .078, power = .679.

Pairwise Comparisons for Seniority

Significant status pairwise differences were obtained in the dependent variable

Seniority between Always PP and PP Demoted. The mean number for group seniority

were 4077.40 for Always PP, 2088.48 for PP Demoted.

Pairwise Comparisons for Post Word Count

Significant status pairwise differences were found for the dependent variable

Post Word Count between Always PP and both PP Demoted and Promoted PP. The

means for this dependent variable were as follows; 249215.93 for Always PP, 96116.79

for PP Demoted and 91324.94 for Promoted PP.

Pairwise Comparisons for Reputation

Significant pairwise comparisons were found for the dependent variable

Reputation for Always PP and PP demoted, PP Inactive and Promoted PP. The means

for these groups were as follows; 3705.33 for Always PP, 1032.72 for PP Demoted,

767.58 for PP Inactive and 1970.71 for Promoted PP.

Pairwise Comparison for Number of Thanks

For the dependent variable Number of Thanks significant comparisons were

found between Always PP and the other levels. The means were as follows; 1468.56

for Always PP, 324.10 for PP Demoted, 468.89 for PP Inactive and 440.35 for

Promoted PP.

Pairwise Comparison for Thread Word Count

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One significant pairwise comparison was found for Thread Count. This

difference was found to be significant for Always PP and PP Demoted. The mean for

Always PP was 60.79 and 24.90 for PP Demoted.

4.6.1.3. Conclusion

The previous section quantitatively analysed all those that rose to status between

2009-2011. This related to the following hypotheses;

H1 – Influential individuals will have a higher number of posts in comparison with

their counterparts

H2 – Influential individuals will have a higher number of threads in comparison

with their counterparts

H3 – Influential individuals will have a higher word count (per post) than their

counterparts

H4 – Influential individuals will have a higher word count (per thread) than their

counterparts

H5 – Influential individuals will have a higher number of thanks from other

community members than their counterparts

Overall, the analyses confirmed a number of hypotheses, the MANOVA confirmed that

there was a difference in the total number of posts, total number of threads, reputation,

word count for threads, number of thanks and average number of thanks between the

status groups. Therefore, H1 that influential individuals would have a higher number

of posts in comparison with their counterparts was supported by the differences found

between the means in the four status groups. Moreover, the post-hoc analysis further

elucidated these differences and revealed that influential individuals had a higher mean

difference in comparison with their counterparts, further supporting this hypothesis.

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With regards to H2 that influential individuals would have a higher number of

threads started in comparison with their counterparts was also supported by the

differences found from the MANOVA. Equally, the post hoc analysis found a

significant difference between influential individuals and all other comparison groups

thus affirming this hypothesis.

However, H3 suggested that influential individuals would have a higher word

count (per post) than their counterparts was only partially supported by the results.

There was no significant difference found for status groups and average word count per

posts (p>0.05). However, total post word count was significantly different. Therefore,

this hypothesis cannot be confirmed as these users do not necessarily have a constantly

higher word count in their posts, but overall they have a higher total word count.

Similarly, H4 that influential individuals would have a higher word count (per

thread) than their counterparts was only partially confirmed from the findings as the

MANOVA. Word count (per thread) or average word count was not significantly

different for the four different groups illustrating that influential individuals did not

have a higher word count in the threads that they started in comparison to the three

other groups. Nonetheless, the total number of words they had throughout all their

threads was significantly higher than their counterparts. Therefore, H4 can only be

partially supported and not accepted, as their thread starting behaviour does not

consistently portray a higher level of words.

H5 suggested that influential individuals would have a higher number of thanks

in comparison to their counterparts. This hypothesis was supported from the

MANOVA and the post hoc analysis. Supporting the notion that those that are

influential within VCs receive more acknowledgement and thanks from others in

comparison with those that are not influential.

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With regards to other DVs that were included the MANOVA and post-hoc

analysis supported the notion that influential individuals a higher reputation than their

counterparts. While this was not hypothesised as explained previously, it confirms the

rationale for separating VC users into four groups.

4.7. Mixed Effects Models

A mixed effects model was incorporated into the analysis to assess posting and

thread starting frequency. As such, due to the groupings (posters within a particular

status group) and the repeated measures nature of assessing multiple variables at

different points in time; a mixed model appeared most suitable to provide a flexible

approach with a variety of correlation patterns to be modelled. Moreover, mixed

models can handle uneven spacing of data particularly with repeated measures. Thus,

making this analysis more appropriate to assess frequency.

This section examines the following hypotheses via mixed effects modelling:

H6 – There will be a relationship between posting frequency and status

H7 – There will be a relationship between thread starting frequency and status

H8 – There will be a relationship between acknowledgement from others and status

4.7.1 Thread Frequency and Status Group

Table 4.9

Descriptive Statistics for Status Group Monthly Thread Frequency

Status Group Mean SD

Always PP 2.48 1.813

PP Demoted .35 .626

PP Inactive .49 .835

100

Promoted to PP .28 .695

4.7.1.1 Fixed Effect for Mixed Model

There was a significant main effect for status and thread frequency, F(3, 2546)

= 96.931, p < 0.001 and a significant interaction for status*month and thread frequency,

F(3, 2546) = 16.556, p < 0.001. This implies that status is an important predictor of

thread frequency and that the relationship between time (months) is moderated by status

groups.

Figure 4.1. Line Graph Showing Mean Threads Each Month for the Status Groups

101

Therefore the intercept for PP Demoted is .024276-.049084= -0.016564 and this

is significantly lower than for Always PP (t=-5.414, p<0.001). Equally, PP Inactive

was significantly lower than Always PP (t=-5.544, p< 0.001), but Promoted to PP was

not significant (t=-1.357, p=.175). This therefore shows a lower thread frequency for

PP Demoted and PP Inactive when compared with Always PP.

Pairwise Comparisons for Status

All pairwise comparisons were significant (at the 0.001 level) for Always PP

and the other status groups. Illustrating that Always PP had a higher thread frequency

when compared with PP Inactive, PP Demoted and Promoted to PP. This supports the

hypothesis that influential individuals start threads more frequently.

4.7.2. Post Frequency and Status Group

Table 4.10

Descriptive Statistics for Status Group Monthly Post Frequency

Status Group Mean SD

Always PP 82.93 27.932

PP Demoted 12.86 10.698

PP Inactive 17.30 11.337

Promoted to PP 8.83 8.324

4.7.2.1 Fixed Effect for Mixed Model

There was a significant main effect for status and thread frequency, F(3, 2546)

= 517.320, p < 0.001 and a significant interaction for status*month, F(3, 2550) =

135.735, p < 0.001. This implies that status is an important predictor of post frequency

and that the relationship between time (month) and post frequency is moderated by

group status.

102

Figure 4.2. Line Graph Showing Mean Number of Posts Each Month for the Status

Groups

The intercept for PP Demoted is 1.095608 – 2.323133 = -1.227525 which is

significantly lower than PP Always PP (t=-18.152, p<0.001). Equally, PP Inactive was

significantly lower than Always PP (t=-11.117, p<0.001) as was Promoted to PP (t=-

3.380, p<0.05). This therefore supports the hypothesis that influential individuals post

more frequently than their counterparts.

Pairwise Comparisons for Status

Pairwise comparisons were significant for Always PP and PP Demoted and

Promoted PP (at the 0.001 level). Therefore, the status level Always PP had a higher

103

mean difference, showing a high posting frequency. This therefore adds support for

the hypothesis that influential individuals post more frequently.

4.7.3. Number of Thanks Frequency and Status Group

Table 4.11.

Descriptive Statistics for Status Group and Number of Thanks Frequency

Status Group Mean SD

Always PP 34.59 17.041

PP Demoted 4.48 3.479

PP Inactive 5.32 3.968

Promoted to PP 3.95 4.304

4.7.3.1. Fixed Effects for Mixed Model

There was a significant main effect for status and number of thanks received,

F(3, 2546) = 135.842, p<0.001 and a significant interaction status*month, F(3, 2546)

= 238.083, p< 0.001. This implies that status is an important predictor of number of

thanks received frequency. In addition, the interaction between status and month

implies that the relationship between month (time) and number of thanks frequency is

moderated by status group.

104

Figure 4.3. Line Graph Showing Mean Number of Thanks Each Month for the Status

Groups

The intercept for PP demoted is 1.389133-1.646879 = -0.257746. This is

significantly lower than for the status group Always PP (t=-25.232, p<0.001). In

addition, PP Inactive was also found to be significantly lower than Always PP (t=-

16.898, p<0.001) as well as Promoted to PP (t=-15.901, p<0.001). This illustrates a

significant difference between the groups and appears to add some support to the

hypothesis that influential individuals receive a higher number of thanks each month in

comparison to their counterparts.

Pairwise Comparisons for Status

All status groups were found to be significantly different from Always PP (at

105

the 0.001 level). Therefore, Always PP consistently had a higher mean difference

when compared to the other groups; illustrating number of thanks was higher for this

particular group; thus supporting the notion that influential individuals receive a

higher level of acknowledgment from others. Moreover, Promoted to PP was found to

be significantly lower that PP Inactive.

4.7.4. Average Number of Thanks Frequency and Status Group

Table 4.12.

Descriptive Statistics for Status Group and Average Number of Thanks Frequency

Status Group Mean SD

Always PP 2.825 1.5449

PP Demoted 3.174 2.8181

PP Inactive 3.992 2.8894

Promoted to PP 2.461 1.4116

4.7.4.1. Fixed Effects for Mixed Model

There was a significant main effect for status and average number of thanks,

F(3, 2368) = 27.338, p < 0.001 and a significant interaction for status*month, F(3,

2368) = 19.188, p < 0.001. This implies that status is an important predictor of average

number of thanks frequency and the interaction suggests that the relationship between

time (month) and average number of thanks frequency is moderated by group status.

106

Figure 4.4. Line Graph Showing the Mean Average Number of Thanks Each Month

for the Status Groups

The intercept for average number of thanks for PP Demoted is -.124935 -

0.51547 = -0.176482. This is significantly lower Always PP (t=-2.985, p<0.05). PP

Inactive was not significant (t=-.431 p=.667) and PP Promoted was significantly higher

than Always PP (t=4.983, p<0.001). Therefore, there is a clear difference among the

levels for status with regards to average number of thanks and Promoted to PP appears

to have the highest number of average number of thanks. This adds some support to

the notion that influential individuals gain more acknowledgement as they are gaining

107

status within a community, though does not fully support the hypothesis in the present

investigation.

Pairwise Comparisons for Status

PP Inactive was the only group that was significantly different from all the other

status groups; equally, this group had a higher mean difference than their counterparts.

This demonstrated that PP Inactive had a higher average number of thanks frequency

than Always PP, PP Demoted and Promoted to PP. Equally, Always PP had a higher

mean difference in comparison with Promoted to PP. However, this does not appear to

support the notion that influential individual receive more acknowledgement from other

community members.

Conclusion for Mixed Effect Models

This section of the analysis related to the following hypotheses:

H6 – There will be a relationship between posting frequency and status

H7 – There will be a relationship between thread starting frequency and status

H8 – There will be a relationship between acknowledgement from others and status

H7 suggested there would be a relationship between posting frequency and

status. The model illustrated a significant main effect for both status and posting

frequency and an interaction between status and time and post frequency, accordingly,

supporting the hypothesis. Equally, the pairwise comparisons from the post hoc test

revealed that influential individuals outperformed those that were promoted and those

that were demoted on monthly postings, further supporting the notion of influential

individuals having a higher post frequency.

Based on the models, H7 there would be a relationship between posting

frequency and status was supported by the significant main effect for group status and

thread frequency. This illustrated the groups had a different mean monthly thread

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number. Moreover, there was a significant interaction between status and time upon

thread frequency. Consequently, supporting the notion that time and thread frequency

was moderated by group status. Upon further analysis, the post hoc test found

significant differences between the influential users and all other group categories.

More specifically, influential individuals had a higher thread frequency.

Hypothesis 8 suggested that there would be a relationship between

acknowledgement from other and group status. Subsequently, there was a significant

main effect for number of thanks and status and average number of thanks and status.

Equally, the mixed effects model illustrated a significant interaction between status and

time for both average number of thanks and number of thanks. This supports the

hypothesis that there is a relationship between group status and acknowledgement from

others. For number of thanks frequency the post hoc test found significant mean

differences for influential individuals and all other categories, this further confirms the

assertion that influential individual receive a greater level of acknowledge in

comparison with their counterparts.

However, the post hoc test for average number of thanks did not reveal

significant mean differences for influential individuals in comparison to the three other

groups. Nonetheless, the test did find significant mean differences for demoted

individuals. That is, those that lost status had a significantly lower mean difference

from their counterparts (Promoted to PP and PP Inactive). This, together with the

number of thanks frequency indicates that influential individuals receive more

acknowledgements from others by way of number of thanks, than their counterparts;

conversely, those that lose their status experience a decline in acknowledgement from

others.

109

4.8 Islamic United Analysis

This section will potray behaviour characteristics for each of the 8 users

discussed in phase one for IU. As such, the charts below illustrate posting frequency,

thread frequency and average word count (per year) to reveal any corresponding

patterns of behaviour amongst individuals when they were deemed to be influetnial in

the community. As previously illustrated in the overview of the chapter, this sections

of the thesis relates to the following hypotheses;

H1 – Influential individuals will have a higher number of posts in comparison with

their counterparts

H2 – Influential individuals will have a higher number of threads in comparison with

their counterparts

H3 – Influential individuals will have a higher word count (per post) than their

counterparts

H4 – Influential individuals will have a higher word count (per thread) than their

counterparts

H5 – Influential individuals will have a higher number of thanks from other

community members than their counterparts

H6 – There will be a relationship between posting frequency and status

H7 – There will be a relationship between thread starting frequency and status

H8 – There will be a relationship between acknowledgement from others and status

Table 4.13

Role transitions for eight IU members over a 24 month period.

Months

IU Member 24 - 18 18 – 12 12 - 6 6 – 0

Members that Gained Reputation (Part One)

110

Gag Order Collaborator Collaborator Leader Leader

Intoodeep Contributor Contributor Collaborator Leader

Pluma Collaborator Leader Leader Leader

Al-Omari Contributor Contributor Contributor Leader

Members that Lost Reputation (Part Two)

Umar247 Leader Leader Contributor Collaborator

Free the Oppressed Leader Leader Inactive Inactive

Iqra Contributor Leader Inactive Inactive

Weshallnotkeepsilent Leader Leader Leader Collaborator

4.8. Global IU Descriptive and Inferential Statistics

This next section examines all of the individuals that gained and lost status within LWP

over a two-year period. In expanding from the previous eight users, this section

includes N = 36 IU online community members, which have been influential at some

point within the cluster analysis as relates to the following hypotheses:

H1 – Influential individuals will have a higher number of posts in comparison with

their counterparts

H2 – Influential individuals will have a higher number of threads in comparison with

their counterparts

H3 – Influential individuals will have a higher word count (per post) than their

counterparts

H4 – Influential individuals will have a higher word count (per thread) than their

counterparts

H5 – Influential individuals will have a higher number of thanks from other

community members than their counterparts

111

4.8.3. Descriptive Statistics

Table 4.14

Group Properties for the Independent Variable Status

Group Identifier Number Status Group N

1 Always Influential 12

2 Influential to Another

Role

10

3 Influential to Inactive 5

4 Promoted to Influential

(without null values)

9

Table 4.15

Group Status Mean and Standard Deviation Scores for Dependent Variable

Reputation

Group Identifier

Number

Mean SD

1 5150.00 3735.21

2 350.00 33

3 0.00 0.00

4 1744.44 1227.92

Table 4.16

Group Status Mean and Standard Deviation Scores for Dependent Variables Post

Total and Post Word Count

Post Total Post Word Count

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Group Identifier

Number

Mean SD Mean SD

1 4110.67 3129.50 29263.75 29214.43

2 825.50 669.66 6152.30 6625.72

3 502.00 408.70 20255.60 38305.19

4 2866.56 2714.61 20269.78 18269.35

Table 4.17

Group Status Mean and Standard Deviation Scores for Dependent Variables Thread

Total and Thread Word Count

Thread Total Thread Word Count

Group Identifier

Number

Mean SD Mean SD

1 115.42 83.71 37659.75 40938.16

2 69.20 84.53 42917.20 87950.83

3 25.60 45.46 10109.00 21310.70

4 74.22 66.31 19586.89 18612.71

Table 4.18

Group Status Mean and Standard Deviation Scores for Dependent Variables Average

Thread Word Count and Average Post Word Count

Average Thread Word Count Average Post Word Count

Group Identifier

Number

Mean SD Mean SD

1 367.4171 404.67439 70.0990 69.06169

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2 368.2107 345.32378 13.2168 20.07446

3 179.4292 176.11280 6.0828 1.95793

4 233.5321 225.02219 10.4569 9.69614

Table 4.19

Group Status Mean and Standard Deviation Scores for Dependent Variable Number

of Thanks Received and Average Number of Thanks

Number of Thanks Average Number of Thanks

Group Identifier

Number

Mean SD Mean SD

1 658.08 503.93 7.0319 3.42860

2 70.40 66.32 108.0820 286.16297

3 1.20 2.168 49.4000 81.18990

4 450.00 352.99 6.1497 3.42082

Note. All missing values (-1) were excluded from number of thanks.

4.8.4. Inferential Statistics

4.8.4.1. Method of Analysis

Analysis of the relationship between the IV (status) and the dependent variables

(seniority, post word count, reputation, number of thanks, average number of thanks,

thread total, thread word count, average post word count and average thread word)

count were analysed using a MANOVA. As previously mentioned, MANOVA is

preferred over a series of ANOVAs due to the increased probability of making a Type

I error.

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With regards to the various assumptions that need to be met, Box’s M was

executed to determine the equality of variance covariance. In this case the Box’s M

result was statistically significant (p<0.001), illustrating significant differences

amongst the levels of the IV (status) in the covariance matrices. However, as sample

sizes in this case are relatively similar in size (though not exact), and acknowledging

the fact that Box’s M is highly sensitive to violations of normality, Levene’s test of

equality of variance was also conducted. Whenever Levene’s test for homogeneity of

variance was significant (at the p<0.05 level), non-parametric tests (Kruskal-Wallis)

were used to confirm the effects of the MANOVA (at the p<0.001 level). In almost all

cases (apart from post word count) Kruskal-Wallis confirmed the findings from the

MANOVA; nonetheless, in cases where one test had a significant result and the other

32comparisons Games-Howell was used due to the uncertainty regarding equal

population variances; thus equal variance and normality could not be assumed.

4.8.4.2. Multivaraite Effects

A one-way MANOVA revealed a significant multivariate main effect for status,

F (27, 71) = 2.821, p <. 001; Wilks’ λ = .118, partial η2= .509. Power to detect the

effect was .999. In addition, a significant intercept was also found via the MANOVA

F (9, 24) = 15.699, p <. 001; Wilks’ λ = .145, partial η2= .855. Power to detect was

1.00. Thus, there was a difference in the means for the status groups with regards to the

various dependent variables under investigation, concluding that individuals in each of

the groups have a different pattern of online behavior from one-an-other and did not

have similar means with regards to the various DVs under investigation.

Significant univariate effects were found for Seniority, F 3, 36) = 5.343, p<0.05, η2 =

.334, power = .404; Reputation F (3, 36) = 10.545, p<0.05, η2 = .497, power = .869;

Number of Thanks, F(3, 36) = 7.294, p<0.05, η2 =.627 and Average Post Word Count

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F(3, 36) = 5.328, p<0.05, η2 = .333 Yet, there were no significant multivariate effects

for post word count, thread total, thread word count, average thread word count and

average number of thanks.

Pairwise Comparisons for Seniority

Significant status pairwise differences were obtained in the dependent variable

Seniority between Always PP and both PP Demoted and PP inactive as well as

between PP Inactive and Promoted to PP. The mean number for Seniority was

4110.67 for Always PP, 825.50 for PP Demoted, 502.00 for PP Inactive and 2866.56

for Promoted to PP.

Pairwise Comparisons for Reputation

Pairwise differences were also obtained for the dependent variable Reputation

between Always PP and PP Demoted, PP Inactive and Promoted to PP. The means

for reputations were as follows; 5150.00 for Always PP, 350.00 for PP Demoted, 0.00

for PP Inactive and 1744.44 for Promoted to PP.

Pairwise Comparison for Number of Thanks

Pairwise comparisons were found to be significant for the dependent variable Number

of Thanks between Always PP and both PP Demoted and PP Inactive. For the PP

Demoted level of the IV differences were found be significant between PP Demoted

and PP Inactive and between PP Inactive and Promoted to PP. The means are as

follows; 658.08 Always PP, 70.40 PP Demoted, 1.20 for PP Inactive and 450.00 for

Promoted to PP.

Pairwise Comparison for Average Post Word Count

Finally, a comparison was found to be significant for the dependent variable

Average Post Word Count between Always PP and PP Inactive. The mean for

Always PP was 70.0990 and the mean for PP Inactive was 6.0828.

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4.8.4.3. Conclusion

The MANOVA investigated a number of hypotheses in relation to group

status. Seniority, average post word count, reputation, number of thanks and average

number of thanks were all found to have significant effects. Thus, leading to the

conclusion that the means for the status groups for those particular DVs were

significantly different from one-an-other. Contrary to that, post word count, thread

total and thread word count did not reveal significantly different means for the four

status groups. With regards to the previously mentioned hypotheses, the above

statistics related to the following:

H1 – Influential individuals will have a higher number of posts in comparison with

their counterparts

H2 – Influential individuals will have a higher number of threads in comparison with

their counterparts

H3 – Influential individuals will have a higher word count (per post) than their

counterparts

H4 – Influential individuals will have a higher word count (per thread) than their

counterparts

H5 – Influential individuals will have a higher number of thanks from other

community members than their counterparts

Hypothesis 1 suggested that influential individuals from IU would have a higher

number of posts in comparison with the other status groups. The MANOVA result in

conjuncture with the post hoc tests illustrated that influential individuals had

significantly higher mean differences than the demoted and promoted group (though

there was no significant difference found for the inactive group), thus this does indeed

support Hypothesis 1.

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Hypothesis 2 suggested influentials would have a higher thread total in

comparison with the other status groups; however, the lack of significance found for

the multivariate analysis does not support this hypothesis.

Alternatively, Hypothesis 3 has only partial support as the word count (per post)

was found to be significantly different for the status groups; specifically, influential

individuals had a significantly higher mean difference than those that became inactive

(but not for the other two groups). However, total post word count was not found to be

significant; thus, Hypothesis 3 cannot be fully supported, but there is evidence to

suggest some difference.

The findings do not support Hypothesis 4 that influential individuals have a

higher word count (per thread) than their counterparts as there was no significant

difference found between the groups for total word count for threads and word count

per thread.

Equally, mixed results were produced for average number of thanks and total

number of thanks. That is, number of thanks was found to be significant; therefore,

there was a difference in the means for the groups with regards to number of thanks

(specifically between Always PP and PP Demoted), which add partial support for

Hypothesis 5 that influential individuals will have a higher number of thanks in

comparison with their counterparts. Yet, there was no significant difference found

between the groups for average number of thanks. Thus, this cannot be fully confirmed.

4.8.5. Mixed Effects Models

A mixed effects model was incorporated into the analysis to assess posting

and thread starting frequency and number of thanks over time. As previously stated

in the LWP section, due to the groupings and clusters of the data (posters within a

particular status group) and the repeated measures nature of assessing multiple

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variables at different points in time a mixed model appeared most suitable to provide

a flexible approach with a variety of correlation patterns to be modelled.

This section examines the following hypotheses via mixed effects modelling:

H6 – There will be a relationship between posting frequency and status

H7 – There will be a relationship between thread starting frequency and status

H8 – There will be a relationship between acknowledgement from others and status

4.8.5.1. Thread Frequency and Status Group

Table 4.20.

Descriptive Statistics for Status Group Monthly Thread Frequency

Status Group Mean SD

Always PP .80 .999

PP Demoted .28 .567

PP Inactive .567 .716

Promoted to PP .72 1.031

4.8.5.1.1. Fixed Effect for Mixed Model for Thread Frequency

There was no significant main effect for status and threads, F(3, 2270) = 1.936,

p = .122. However, there was a significant interaction between status*month, F (1,

2270) = 7.982, p < 0.001. This implies that status is not a predictor of thread frequency,

but that there is a relationship between time and thread frequency which is moderated

by status group.

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Figure 4.5. Line Graph Showing Mean Number of Threads Started Per Month for Each

of the Status Groups

Therefore the intercept for PP Demoted is .588886-.030823 = 0.558063 and that

this is significantly lower than for Always PP (t=-4.402, p< 0.001). Equally, PP

Inactive was significantly lower than Always PP (t=-2.658, p< 0.05), however,

Promoted to PP was not significantly different (t=-1.638, p=.101). This therefore

shows a lower thread frequency for PP Demoted and PP Inactive but not for Promoted

to PP when compared with Always PP. As such, the interaction between month thread

frequencies does appear to be moderated by group status.

Pairwise Comparisons for Status

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Always PP and Promoted to PP appeared to have a higher mean difference

(for each month) with regards to thread frequency in comparison with their

counterparts. This adds support for the hypothesis that influential individuals initiate

threads more frequently than those who are losing influence within an online

community. However, status on its own is not a predictor of thread frequency, status

is a moderating variable between time and thread frequency only (see section

4.8.5.1.Thread Frequency and Status Group).

4.8.5.2. Group Status and Post Frequency

Table 4.21.

Descriptive Statistics for Status Group Monthly Thread Frequency

Status Group Mean SD

Always PP 20.14 14.865

PP Demoted 4.35 4.35

PP Inactive 3.448 3.448

Promoted to PP 20.20 12.492

4.8.5.2.1. Fixed Effects for Mixed Model for Post Frequency

There was a significant main effect for status and number of posts, F(3, 2270)

= 61.521, p < 0.001 and a significant interaction for month*status, F(1, 2270) = 67.677,

p < 0.001. This illustrates that status group is a predictor of number of posts and that

the relationship between number of posts and time is moderated by status group.

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Figure 4.6. Line Graph Showing Mean Number of Posts Per Month for Each of the

Status Groups

Therefore the intercept for PP Demoted is 8.286013-1.035868= 7.250145. This is

significantly lower than for Always PP (t=-12.406, p< 0.001). Equally, PP Inactive

was significantly lower than Always PP (t=-4.964, p< 0.001) as was Promoted to PP

(t=-11.593, p< 0.001).

Pairwise Comparisons for Status

Always PP and Promoted to PP appeared to have a higher post frequency in

comparison with their counterparts. This adds support for the hypothesis that

influential individuals post more frequently than those who are losing influence

within an online community.

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4.8.5.3. Total Number of Thanks

Table 4.22.

Descriptive Statistics for Group Status and Monthly Number of Thanks

Status Group Mean SD

Always PP 3.97 7.120

PP Demoted .44 1.148

PP Inactive .02 .149

Promoted to PP 2.50 4.923

4.8.5.3.1. Fixed Effect for Mixed Model

There was a significant main effect for status and number of total thanks

received, F(3, 2270) = 31.087, p < 0.001 and a significant interaction between

status*month, F(1, 2270) = 117.753, p < 0.001. This implies that status is an important

predictor of total number of thanks received. Additionally, the interaction reveals that

the relationship between time and total number of thanks is moderated by group status.

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Figure 4.7. Line Graph Showing Mean Number of Thanks Per Month for Group Status

Therefore the intercept for PP Demoted is .641972 – .556892 = 0.08508 and

this is significantly lower than for Always PP (t=-18.223, p< 0.001). Equally, PP

Inactive was significantly lower than Always PP (t=-7.171, p< 0.001), as was Promoted

to PP (t=-8.087, p< 0.001). This therefore shows a lower total number of thanks

received for PP Demoted, PP Inactive and Promoted to PP when compared with Always

PP. As such, status group does appear to be a predictor of number of thanks.

Pairwise Comparisons for Status

All pairwise comparisons were significant (at the 0.001 level) apart from PP

Demoted vs PP Inactive (p>0.05). Equally, Always PP had a higher significant mean

difference in comparison with all other groups illustrating that this group in particular

had a higher total number of thanks received. Moreover, those that were demoted had

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a lower mean difference when compared to all groups apart from PP Inactive

illustrating that individuals in this group had a higher total number of thanks.

4.8.5.4. Average Number of Thanks

Table 4.23.

Descriptive Statistics for Group Status and Monthly Average Number of Thanks

Status Group Mean SD

Always PP 6.9770 10.18039

PP Demoted 2.2016 1.65236

PP Inactive 3.2000 .83666

Promoted to PP 6.1192 7.77978

4.8.5.4.1. Fixed Effect for Mixed Model Average Number of Thanks

There was a significant main effect for status and average number of thanks

received, F(3, 682) = 20.493, p < 0.001 and a significant interaction between

status*month, F(3, 682) = 14.517, p < 0.001. This implies that status is an important

predictor of average number of thanks received and the relationship between time and

average number of thanks is moderated by group status.

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Figure 4.8. Line Graph Showing Mean Average Number of Thanks Per Month for

Group Status

Therefore the intercept for PP Demoted is -1.176069 + .956235 = -0.219834

and this is significantly higher than for Always PP (t=5.805, p< 0.001). However, PP

Inactive was not significant (t=1.013, p=.311), neither was Promoted to PP (t=-1.462,

p=.144).

Pairwise Comparisons for Status

The only significant pairwise comparisons were found between PP Demoted

vs Promoted to PP and PP Demoted vs Always PP. These comparisons show that

Always PP had a higher average number of thanks in comparison with the demoted

group. Moreover, those that were promoted also had a higher average number of

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thanks in comparison with the demoted group. This adds partial support for the

notion that individuals who gain kudos in a community received more frequent

acknowledgement from others.

Conclusion for Mixed Effects Models

The mixed effects models tested the following hypotheses:

H6 – There will be a relationship between posting frequency and status

H7 – There will be a relationship between thread starting frequency and status

H8 – There will be a relationship between acknowledgement from others and status

Hypothesis 6 indicated that a difference between posting frequency and status

groups, which was supported by the significant main effect for status group and

posting frequency. Similarly, status was found to have a moderating relationship on

time and posting frequency. Further support for Hypothesis 6 is found in the post hoc

analysis where influential individuals were found to have a significantly higher mean

difference (as did those that were promoted) which supports the prediction of a

relationship, specifically influential individuals post more frequently.

Hypothesis 7 predicted a relationship between group status and thread starting

frequency. However, there was no significant main effect found to support such a

prediction for status and thread frequency; though, there was a relationship found for

time and thread frequency which was moderated by group status. Equally, the post-

hoc analysis found significantly higher mean differences for promoted individuals and

always influential individual, which adds support for Hypothesis 7.

Hypothesis 8 suggested that there was a relationship between acknowledgement

and group status, based on the number of thanks that particular individuals receive.

There was indeed a relationship found between number of thanks frequency and status

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and status was also found to be a moderating variable for time and number of thanks

frequency. This supports the prediction made for Hypothesis 8. To further corroborate

this notion, there was also a significant main effect found for the status groups with

regards to average number of thanks and an interaction between time and group status

for average number of thanks. This implies that status is an important predictor of

thanks received and the relationship between time and number of thanks. Once again,

the post hoc test found the greatest difference in the influential category, indicating that

influential individuals receive a higher level of number of thanks on a frequent basis.

Moreover, those that are demoted or have lost some level of respected status receive

far less acknowledgement or thanks from other community members; thus supporting

Hypothesis 8.

4.9. Comparing Influence between Communities

As illustrated above, there are been some (slight) differences in the results

between IU and LWP. As such, this section will examine influential individuals across

both communities to assess whether there are notable differences in the social structure

for those that have been categorised as “Always PP” or influential individuals.

Specifically, this will address whether influentials in IU have more connections than

influentials in LWP; moreover, this will also assess how influential connecting vertices

are with regards to the influential individuals. Therefore, this addresses that those that

were always influential throughout the cluster analysis had similar types of influence

within their online communities.

This section relates to the following hypotheses:

H9 – Influential members of IU will have more connections to other members in

comparison with influential members of LWP

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H10 – Influential members in LWP will have more connections with other influential

members than in IU.

4.9.1. Method of Analysis

Betweenness Centrality was used to assess level of connections between the two

online communities. However, in relation to the parametric assumptions, the data did

not have normality of distribution and had violated the assumption of variance as the

Levene’s test revealed a significant result (p > 0.05). As such, a Mann Whitney U Test

was conducted to assess the differences in means for Betweenness Centrality for all

those that had been categorised as “Always Influential” across both IU and LWP.

Alternatively, to investigate the level of influential neighbours for specific vertexes,

PageRank was used. This assessed how influential ones friends/neighbours are. In

relation to the parametric assumptions, the data did have normality of distribution and

variance was not violated; thus an independent samples t-test was employed for a

comparison of means for influential individual’s PageRank.

4.9.1.1. Betweenness Centrality

Table 4.24

Betweenness Centrality Descriptive Statistics for Influential Individuals of IU and

LWP.

Community N Median Range

Islamic United 13 4.7420 9.18

Left-Wing Politics 43 4.7070 10.63

There was no significant difference found for Betweenness Centrality for the

influential individuals in IU and LWP U=180, p = .0535. Therefore, the hypothesis

5 Not significant at the 0.05 level

129

that IU will have more connections to other members in comparison with influential

members of LWP cannot be confirmed.

4.9.1.2. PageRank

Table 4.25.

PageRank Descriptive Statistics for Influential Members of IU and LWP.

Community N Mean SD

Islamic United 13 4.7409 2.59229

Left-Wing Politics 43 5.2322 2.53372

There was no significant difference found between the online communities for

PageRank t(54) = -.609, p=.539. Therefore, Hypothesis 10 that influential members in

LWP have more connections with other influential members than in IU is not supported

by the findings in this investigation.

4.10. Chapter Summary

For the online community LWP it appears from the MANOVA that those that

were categorised as “Always PP” or indeed influential individuals had a higher number

of posts, threads, word count for threads, number of thanks, reputation and average

number of thanks in comparison with those that were promoted, those that were

demoted and those that became inactive. Therefore, this confirms Hypotheses 1 – 5.

Group status was also found to be a significant predictor of post frequency. The

interaction between time and status suggests that status is a moderating variable

between time and post frequency. With regards to the pairwise comparisons that were

found to be significantly different from each other, Always PP had a significantly

130

higher mean difference than Demoted PP and Promoted to PP (but not PP Inactive).

This therefore suggests that influential individuals do post more frequently than their

counterparts, which adds partial support for Hypothesis 6.

With regards to the notion of online behaviour and frequency, the mixed effects

models provide support for the notion that influential individuals start threads more

frequently than their counterparts. This was illustrated by the significant main effect

for status and thread starting frequency, as well as the significant interaction between

time and status for thread frequency. Consequently, this suggests that there is indeed a

relationship between status and thread starting frequency; more specifically, status is a

moderating variable in time and thread frequency. Equally, the pairwise comparisons

revealed that Always PP had a significant higher mean difference than PP Inactive, PP

Demoted and Promoted to PP, which supports the predictions made and confirms

Hypothesis 7.

Group status was found to be a predictor of number of thanks frequency and

equally was found to be the moderating variable between time and number of thanks

frequency. Moreover, the biggest pairwise comparisons were found for Always PP as

this category had a higher number of thanks frequency in comparison with all other

levels; however, PP Demoted was not found to have a lower mean difference in

comparison with Promoted to PP or PP Inactive. As such, this supports the notion that

influential individuals receive more acknowledgements from others on a regular basis,

but does not confirm the hypothesis that those that lose their status endure a noticeable

decline in acknowledgement. Therefore, this only partially supports Hypothesis 8.

The final analysis for LWP examined average number of thanks frequency.

Once again, status was found to be a significant predictor of average number of thanks

frequency. However, the pairwise comparisons found the greatest difference to be

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within the PP Inactive category as this group had a higher mean difference in average

monthly thanks in comparison with Always PP, PP Demoted and Promoted to PP,

which does confirm Hypothesis 8 that there is a relationship, but does not support the

notion that influentials receive a higher level of average number of thanks frequency.

Alternatively, IU revealed slightly different results. The MANOVA found the

status groups to have different means for the following dependent variables; seniority,

average post word count, reputation and number of thanks. With regards to the pairwise

comparisons, Always PP was found to be significantly different from at least one other

group in all of the significant dependent variable illustrating that influential individuals

have a higher total number of posts, average word count, reputation and number of

thanks in comparison with their counterparts. Therefore, this confirms Hypothesis 1,

Hypothesis 3, Hypothesis 4 and Hypothesis 5. Nonetheless, Hypothesis 2 could not be

confirmed as there was no significant difference found between the groups for thread

starting, thread word count or average thread word count.

Status was found to be a predictor of posting frequency and also the interactions

between time and status suggests that status is a moderating variable. Equally, the post

hoc analysis revealed that Always PP and PP Promoted had a significantly higher mean

than both PP Inactive and PP Demoted. Consequently, this supports Hypothesis 6 that

there is indeed a relationship between status and posting frequency; more specifically,

influential individuals post more frequently than their counterparts.

In accordance with the latter point, the mixed effects model also produced

different results to that of LWP. Namely, status was not found to be a predictor of

thread frequency. However, this was found to be a moderating variable between time

and thread frequency due to the interaction effect that was found between status and

month (See Section 4.8.5.1.Thread Frequency and Status Group). There was a

132

significant difference found in the post-hoc analysis for status and thread frequency as

Always PP was found to be significantly different found PP Inactive and PP Demoted,

suggesting that individuals that have always been influential throughout the cluster

analysis start threads more frequently than others. This adds some support for

Hypothesis 7.

Likewise, status was also found to be a predictor of number of thanks frequency

and also a moderating variable between time and number of thanks. Once again,

Always PP and Promoted to PP had a significantly higher mean difference than their

counterparts (PP Demoted) illustrating some support for the notion that influential

individual received more acknowledgement from other in the community; equally, this

does provide support for the notion that those that are losing reputation or status within

the community experience a decline in acknowledgment and thanks from others.

Therefore, this adds support for Hypothesis 8.

Finally, status was found to be a predictor of average number of thanks

frequency and a moderating variable between time and average number of thanks. The

pairwise comparisons only revealed significant mean differences for Always PP vs PP

Demoted and Promoted to PP vs PP Demoted. Therefore, the demoted category

experienced a significantly lower mean difference than those that are influential. While

there was no significant pairwise comparison between PP Inactive and PP Demoted,

this still supports the notion that individuals who lose reputation in a community receive

less acknowledgement and thanks from others (especially in comparison with those that

are gaining or sustaining status). Consequently, this information, alongside the results

from number of thanks frequency, appears to support Hypothesis 8.

Overall, the results from IU and LWP appear relatively similar; though, there is

only one noticeable difference in the results. Thread behaviour was found to be

133

significant for influential individuals of LWP, but was not significant for influential

individuals in IU. Interestingly, this suggests that influential individuals in LWP start

threads more frequently and have longer threads, than do influential individuals in IU.

Aside from thread behaviour, the results remain relatively similar across both

communities. Consequently, this confirms the notion the influential individuals (or

individuals gaining momentum within a community) post more frequently and with

longer posts. Equally, they receive a higher number of thanks from other members on

a regular basis. See table below for hypotheses summary table.

Table 4.26

Hypothesis Summary Table

Hypothesis Finding

Hypothesis 1:

Influential individuals will have a higher number of posts in

comparison with their counterparts

Supported

Hypothesis 2:

Influential individuals will have a higher number of threads in

comparison with their counterparts

Supported

Hypothesis 3:

Influential individuals will have a higher word count (per post)

than their counterparts

Supported

Hypothesis 4:

Influential individuals will have a higher word count (per thread)

than their counterparts

Supported

Hypothesis 5: Supported

134

Influential individuals will have a higher number of thanks from

other community members than their counterparts

Hypothesis 6:

There will be a relationship between posting frequency and

status

Partially

Supported

Hypothesis 7:

There will be a relationship between thread starting frequency and

status

Supported

Hypothesis 8:

There will be a relationship between acknowledgement from

others and status

Partially

Supported

Hypothesis 9:

Influential members of IU will have more connections to other

members in comparison with influential members of LWP

Supported

Hypothesis 10:

Influential members in LWP will have more connections with

other influential members than in IU

Supported

135

CHAPTER FIVE

RESTRICTED ACESS

136

CHAPTER SIX

RESTRICTED ACESS

.

137

CHAPTER SEVEN

GENERAL DISCUSSION

The aim of this thesis was to identify influential individuals in online

communities and explore their online identities. In doing so, the research examined

various online behaviours that were associated with influential individuals via

numerous different quantitative techniques and approaches. This therefore, ultimately

led to an in-depth qualitative analysis of a select amount of users to examine their rise

and fall from influential status to illustrate the ways in which they lost influence and

followership within two different online communities. Furthermore, this examined the

ways in which they interacted with other users and evolved over time.

The findings from the quantitative data analysis confirmed a number of

hypotheses. Specifically, that there was a difference in the total number of posts,

threads, reputation, word count for threads, number of thanks and average number of

thanks between the influential individuals and other members of the community.

Therefore, those that had always been influential or daubed as reputable engaged in

these behaviours and scored higher in the analysis in comparison with their

counterparts. However, hypothesis 3 relating to average word count (per post) was not

found to be statistically significant for high ranking individuals; however, total word

count post was higher in consistently influential individuals in comparison with their

counterparts. Therefore, high ranking individuals do indeed have a higher overall word

count for their posts, though this is not consistent with each of their posts as their

average word count (per post) was not found to be significantly higher than their

counterparts.

In relation to the latter point, average word count (per thread) was not found to

be statistically significant for influential individuals. However, similar to the previous

138

point, overall word count for threads were found to be statistically significant for

influential individuals in comparison to their counter parts. To simplify, influentials

generally have a higher total word count for their threads, but not an average word count

per thread. Therefore, their threads are not consistently higher than their counterparts.

Nonetheless, Hypothesis 5 was also confirmed; thus influential individuals also

received more thanks in comparison with their less influential counterparts from the

other status groups.

There was a main effect for status and posting frequency, thread frequency and

number of thanks received. However, the results did not fully support the hypothesis

for average number of thanks. To reiterate, there were significant main effects found

for both status and posting frequency and thread frequency. Yet, the notion that

influential individuals would receive more acknowledgement from others was not

consistently supported as the post hoc analysis did not reveal differences between

influential individuals and the other status categories; though, there was a main effect

found suggesting that individuals that are influential do receive more acknowledgement

from others over the months.

On the contrary, results from IU were different from that of LWP which was a

surprising result in itself. Accordingly, there was only support for the notion that

influential individuals post more (and more frequently) than members with different

social roles within their communities; however, there was no support to corroborate

that influential individuals start more threads and initiate conversation more than others

in their online communities; thus, disputing Hypothesis 2. Equally, the results did not

support the notion that word count and average word count were higher from those with

reputable status within IU. Also, there was no support for the notion that high ranking

individuals post more threads, nor that they have longer threads in general.

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Additionally, there was mixed support for acknowledgement from others, in

that, number of overall thanks was supported, but average number of thanks was not

found to be significant in the MANOVA. However, there was a relationship found for

number of thanks and average number of thanks, which contradicted the results from

the previous online community; thus, suggesting different online behaviours are needed

for different ideological online forums.

The findings from the qualitative empirical chapters derived a number of themes

significant to influentials in online forums. This illustrated the journey that influentials

engage in when entering a new forum, gaining reputation within a particular online

forum and subsequently how they lose their following and influence with regards to

other online community members. In turn, the qualitative analysis displayed that all

individuals that were analysed in the LWP forum engaged in acceptance seeking

behaviours when first entering an online forum. Thus, attracting attention and getting

noticed by others is the first step to gaining some momentum within online

communities and establishing ones place within a given online community. This relates

to previous literature as one of the ways in which this was established was to divulge

information on particularly skilled areas or ideological values which may correspond

with the goals of the community (Gladwell, 2002; Hilligoss & Rieh, 2008; Hovland et

al., 1953, Young, Komlodi, Rozsa, & Chu, 2016). Ultimately, these attributes

determine online users’ perceptions of credibility, which affects status and credibility

online.

However, in expanding previous research in online communities the qualitative

research illustrated that while their credentials and expertise were often disseminated

to other users when joining a forum, new individuals (that rose to high ranking status)

also engaged in advice-seeking behaviours. Thus, it is assumed that this behaviour

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would ultimately help seek out those that were already established and credible within

the forum. Still, it could also be interpreted that these individuals were contributing to

the community to avoid any perceptions of “free riding” as discussed by previous

literature (Fan et al., 2009).

In relation to previous research, Rogers (1962) found that opinion leaders

consisted of a number of typical characteristics; these were high levels of social

participation, high social status within a given community and high levels of

responsibility. With regards to the present research, influential individuals did

demonstrate high levels of social participation and status within their online

communities. Moreover, the qualitative analysis did demonstrate that online

influentials did exhibit elements of social responsibility and a need to delegate to others,

which relates well and further corroborates earlier research

Nonetheless, one of the main communications characteristics amongst

influentials when they were particularly influential was a need to display knowledge

within a certain area. This also supports a vast amount of previous literature on

knowledge sharing (Childer, 1986; Ventraman, 1990; Weimann et al., 2007);

furthermore, with regards to IU and LWP those that were deemed influential or

particularly reputable to others in their designated communities displayed knowledge

and offered advice to their following; demonstrating some skill or expertise within their

area (Zang & Dong, 2008). Equally, particularly within IU high ranking individuals

did engage with social activities; thus, their experiences were not predominantly related

to online, which supports the research of Zang and Dong (2008). Interestingly however,

individuals often appeared to actively recruit and rally for individuals and support

within the IU community specifically. This behaviour was not solely for online

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purposes demonstrating that social activities and recruitment was for offline purposes

also.

There is also evidence which supports the need for credibility and how to attain

credibility in online forums. For example, Metzger and Flanagin (2013) discuss source

credibility in relation to the work of Artistotle and rhetoric. Thus, it appears that

individuals do use linguistic devices such as logos, ethos and pathos when particularly

influential within a community within their threads and posts. Therefore, the way in

which people speak (or type) may be just as important as the words that are spoken as

all community members in the narrative analysis utilised these strategies when they

were perceived as influential.

With regards to identifying influentials within online communities specifically,

tracking potential influence illustrated that those that were characterised as influentials

in the online communities, did indeed have numerous connections to social ties to

others in comparison with when they did not have their status (Bakshy et al., 2011; Cha

et al., 2010; Leavitt et al., 2009). Likewise, these users did have high levels of

connectivity and centrality within their networks (Anger & Kittl, 2011; Bakshy et al.,

2011; Cha et al., 2010) which supports the notion and current research with regards to

utilising this particular measurement to track supposed influence or status within online

communities which has been previously criticised (Kwak et al., 2010).

Equally, credibility and expertise did appear with almost every reputable

individual when they had high ranking status; thus, corresponding with the notion that

online influentials do display many of the characteristics of opinion leaders (Gladwell,

2002). Equally, Anger and Kittl (2011) stated that the quality of the relationship was

far more important than the number of connections with regards to popularity. In

expanding upon that notion, the present research found that reputable individuals often

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had a vast number of ties but also displayed strong relationships with others and

identified as particularly close friends to them. Thus, the quality and number of

relationships lead to online behaviours and characteristics that are associated

influentials within online communities.

Equally, Agarwal et al. (2008) found influential bloggers often report items that

may be junk or irrelevant in content. Accordingly, particularly in the IU community

there were instances were high ranking individuals often posted relevant material but it

was often more of a “sharing” activity. Likewise, in the LWP community repeated

“sharing” threads did often lead to a lack of responses and were usually around the time

that users were losing their status within the community. As such, while it cannot be

determined that this was a casual factor in their demise; it was a noted pattern of

behaviour, which could be related to their diminished popularity.

It must also be stated that this research examined those that were popular but

became inactive and expanded on previous research. This particular status group did

provide some noteworthy findings in relation to online behavior. Previously, Agarwal

et al. (2008) had examined influential bloggers that were active and those that were

influential but not active; as such, it is suggested that research includes those that were

active but became inactive to ultimately avoid misrepresentation in samples as these

individuals may become inactive for a number of different reasons. Furthermore,

research should examine roles over time as influentials may move roles or indeed as

illustrated in the qualitative analysis become active once again; thus, should be included

into sample designs.

With regards to distinct behaviours and predictors of influentials included

sociability, responsibility, cultural values, trust, and sheer volume of communication

(Cassell et al., 2005; Cassell et al., 2006; Greer & Jehn, 2009). Sociability was clearly

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relevant as almost all individuals displayed elements of sociability while influential.

Evidently, this is in line with previous literature in that influentials did display levels

of gregariousness when their tenure had been established within an online community

and supports Troldahl and van Dam (1965) and Booth and Babchuk (1972).

Consequently, there are various inconsistencies that this research has not been able to

address; namely, are socio-economic aspects as important in virtual contexts as they

are with research in this area/?

However, a particularly interesting aspect of the analysis was that individuals

that became influential also had an eagerness to follow at the beginning of their

community lifespan; thus, demonstrating that social identities are not fixed. Moreover,

individuals, even influentials, can swap roles -and partition aspects of their social

identities (Eagly et al., 2003; Somech, 2006). This was illustrated in the way that

individuals reverted back to asking questions in phase two of the analysis and also

began to break away from their previously well-established social identities. Thus, this

demonstrates that online influentials are indeed a part of a larger online process.

Indeed, this does add some contribution to existing literature in that it does illustrate

that influentials are part of a wider online social process, one which is fluid in nature

rather than stable as much research does proclaim.

As exhibited in study two influentials enacted behaviours that ultimately

endorsed and promoted the sense of identity within the group. Seyranian (2014) found

language use to promote a strong sense of identity, which was supported throughout

both communities included in the qualitative analysis. Both LWP and IU often utilised

inclusive language and referred to other members as “comrade” or “brother”

demonstrating that they identified as part of the group and displayed behaviours

congruent with this identity. Moreover, it was apparent in the losing status chapter as

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influentials tended to separate more with group norms and prototypes more when

coming towards the end of their reign of reputation, which coincides with existing

literature on social identity theory (Everett et al., 2015; Tajfel, 1982).

Similarly, the way in which people changed roles and status in the quantitative

analysis illustrated that individuals did not remain influential or popular throughout

their community lifespan. To substantiate this claim, the qualitative analysis also

demonstrated instances of people entering the forum and making their presence known

in their acceptance seeking behaviours. Likewise, there were specific instances of

individuals challenging authority figures in the community. Thus, social identities are

fluid in nature.

Consequently, this research has not only qualitatively explored identification

markers for influential individuals in online communities and substantiate that of

previous analytical claims via a new perspective. But, this research has also highlighted

possible ways in which an influential can lose their status within a online community.

Thus, through a social identity approach to online identities this research has

highlighted the importance of group membership and collective identity; as well as

discussing tactics on how to undermine an influential individual in an online

community.

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CHAPTER EIGHT

LIMITATIONS, CONCLUSIONS, FURTHER RESEARCH AND

RELFECTIONS

8.1 Limitations

However, this research is not without its limitations. The first major limitation

to the research is the fact the researcher used secondary data analysis of existing data

for the initial stage of the empirical research (Cheng, 2014). Therefore, the researcher

in the present investigation did not collect the time sliced data or the meta-data from

the online communities, but collected all the qualitative data in the second phase.

Therefore, individuals were already categorised via algorisms as most likely to be

influentials in the particular forums based on certain behavioural and sematic

characteristics in time sliced data over a two-year period. Nonetheless, from this the

researcher then re-categorised online community members as “always influential”,

“influential then demoted”, “influential to inactive” or “promoted to influential” based

on how their roles changed throughout the two-year period as this was necessary for

the hypotheses in the present investigation.

Secondary data analysis does have some implications on the reliability of

research as it has been known for researchers to occasionally omit crucial aspects of

data when not included in the initial collection process due to a lack of thorough

understanding (Cheng, 2014; Johnston, 2014). However, the researcher was present

and aided in aspects of the research that the data set was originally intended for; thus,

has a high level of understanding. Reciprocally, the researchers for the original

research paper also aided in the present research during the quantitative aspects of the

empirical data. More so, the research paper has been published and well established

since publishing their finding on the original data (Davison et al., 2018).

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In addition to the latter point, using an existing data set allowed for in-depth

analysis and accelerated the pace of the research for the researcher (Cheng, 2014;

Doolan & Froelicher, 2009; Johnston, 2014). Equally, this was only one aspect of the

empirical data as select individuals were then investigated further in the qualitative

analysis. In that, 16 online community members were studied in-depth throughout their

entire community lifespan to further determine whether they had certain behavioural

characteristics and online behaviours associated with influentials as the quantitative

analysis was time-sliced and limited to a two-year period. Therefore, mixing methods

added some triangulation and validity to the results.

Whilst examining online communities members throughout the qualitative

aspect of the analysis, threads opposed to posts were examined. This was due to

numerous time constraints on the research as well as the issue that posts pose with

coherency. More specifically, the data set had each individual users’ posts but they

were not in a coherent timeline with the other posts from other users, thus, only that

individual users’ posts could be seen in the database. Equally, there are instances where

posts are deleted, making it difficult to follow the conversational flow. By examining

threads that individual users started, opposed to posts, the researcher was able to see

the progression of conversation and examined the posts from others and the reactions

that each user under examination had to posts; thus, more of the context of the

conversation was preserved (Holtz, Kronberger, & Wagner, 2012).

Likewise, it was shown that those who were influentials had a high frequency

of posting and thread starting behaviours as displayed by the quantitative aspect of the

empirical chapters; also adding further verification and validity in opting this method.

Whilst analysing threads, posts are also examined to further corroborate notions derived

from the qualitative analysis which appears to support the methods used for other online

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community research (Holtz et al., 2012; Myneni, Fujimoto, Cobb, & Cohen, 2015).

Therefore, it was hoped that this would improve overall interpretations of data and

improve the credibility of the research (De Vos. 1998; Denzin, 1978). It should also be

noted that previous research has focused on threads and thread originators to track

influence, thus further rationalising this methodological choice for data collection

(Zhao et al., 2013).

Another interesting aspect derived from the qualitative analysis was that

undermining others or engaging in mocking behaviours tended to gain support from

followers. Given the time constraints on the research and the continuity of the thesis,

further investigation was not achievable but has illustrated strong caveats and

noteworthy findings for future research to examine how to experimentally manipulate

behaviours and whether this affects the perception of onlookers. This could specifically

address the importance of mocking behaviours, undermining fact and authenticity when

gauging credibility amongst online community users.

This research examined two different forums; Islamic United and Left-Wing

Politics. Indeed, these two forums are based on rather different ideological foundations

but similar findings emerged in both aspects of the empirical research. This may act as

some form of data triangulation for the research (Korstjen & Moser, 2018). Korstjen

and Moser (2018) state using multiple sources of data can improve the overall

credibility of research, in this instance data triangulation was achieved by observing

similar findings with regards to influential behaviours and also how individuals

behaved when entering new forums or engaging in “newbie behaviours.” This is

further reinforced in the initial part of the empirical findings; thus the research

demonstrated some level of consistency amongst the findings with regards to posting,

liking behaviours, and word length and as previously stated there were no significant

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difference found between the data of the two groups from the results (Langdridge,

2014).

However, this may also act as a limitation to the research as previous research

posits that individuals have a stronger identification with political ‘partysm’ than other

social identifiers such as ethnicity, religion or race (Westwood et al., 2017). This is

due to the voluntary nature of political party group affiliation; whereas, other social

groups are ascriptive or assigned from birth (Billig & Tajfel 1973; Martinovich, 2017;

Tajfel & Turner 1979; Westwood et al., 2017). Thus, it may be that different attachment

styles (such as bond based or identity based attachments; Ren, 2011; 2012) may be

apparent between the forums as the concept of group identity plays an implicit role in

motivating members to join a particular forums.

Nonetheless, this research did attempt to address some potential differences in

homogeneity within the forums through analysis in the first phase of research. This

was done via analysis on the differences within communities and their influence and/or

connections to other users. The t-tests were conducted to assess whether there were

notable differences in attachments and no significant differences were found. Thus,

this does show that the communities were relatively similar from a strictly positivist

stance.

Furthermore, this research extended knowledge within the influence field by

examining online behaviours that influentials displayed when at their height of power,

for example posting, thread starting and thanking behaviours. Additionally, it also

illustrated how word length was also a prominent indicator to substantiate previous

research (Agichtein et al., 2008). However, this has been conducted across different

online communities rather than focusing on one particular forum and has enabled the

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researcher to replicate these findings via data triangulation, despite the differences in

the philosophical underpinning of the forums (Martinovich, 2017).

There are also several limitations to the use of the internet for data collection

and the various challenges that this encompasses. Firstly, it must be mentioned that the

very nature of the internet it’s dynamic and every changing reality creates issues as

aspects of texts may be incomplete or have changed since writing this thesis (Burke,

Dehne, Rau-Chaplin, & Robillard, 2017). Additionally, the internet in all its glory has

a unique of issue of hacking; thus, users may have been victim to internet crime at some

point, which must be noted (Moore, Clayton, & Anderson, 2009). This may also pose

issues for data transmission as certain excerpts of text may have been corrupted when

taking the information from the online forums themselves as well as issues with

missing, deleted or edited text within threads (Benfield & Szlemko, 2006).

8.2. Future Directions and Implications

Both phases of the research have exposed some noteworthy questions and ideas

for further research. Firstly, there seems to be a vast amount of knowledge contribution

with regards to the forums. In other words, influential individuals offered more

information to others in forums and shared their knowledge. Indeed, this may be seen

as an aspect of their credibility or a way of gaining recognition as demonstrated in the

qualitative analysis or could merely be a posting opportunity as seen in the quantitative

analysis. However, it is difficult to determine knowledge contribution and the

rationalisation for divulging information in online communities and this research was

unable to do so. Nonetheless, attempts to assess posting frequency and length were

examined; though, future research may want to address this further in conjuncture with

reputation.

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This thesis has also revealed differences within the two communities LWP and

IU, this was disclosed in both aspects of the empirical research. Equally, the ideological

underpinnings of each of the forums may well have an impact on the different

attachments that are formed and this is an important point to consider for future

research. Whilst there were some similarities in behavioural characteristics, it is

understandable to consider that the forums may evoke different attachment style due to

the ideological polarization. In others words, do influential members of IU have more

bond-based connections to others? This comes from the notion that political beliefs

may indeed form different types of attachments opposed to religion.

Matinovich (2017) posits that Americans have a stronger attachment to their

political parties opposed to other social identifiers due to the voluntary nature of group

affiliation and ‘partysm.’ Building from this, Martinovich’s research was only relevant

to America, which is a relatively homogenous society (Newport, 2017). Though,

results have been consistent with nations such as Belguim, Spain and the United

Kingdom (Westwood et al., 2017), illustrating the importance of “partysm” with

regards to group identification (Greene, 2002; Huddy, Lilliana, & Lene, 2015). Indeed,

different attachment styles were touched upon in this thesis but more investigation into

whether different social identifiers causes different attachments to others needs to be

investigated further.

With regards to implications, this thesis has highlighted numerous ways in

which influentials could potentially be disrupted within online communities. For

example, qualitative exploration has elucidated ways to potentially gain momentum,

which could in fact replace another’s status. Moreover, it has illustrated that anyone

can become influential based on certain behavioural traits. Indeed, security sectors and

government agencies may want to research this particular area further as it has practical

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implications for government strategy and may help aid policies on counter-terrorism by

undermining credibility in online recruitment forums. This could therefore be

transformed into online-terrorist interventions.

The results may also serve community managers regarding the notion of status

within online communities. It has been illustrated that one does not necessarily need

to be in a position of power to assert some influence over others in online forums.

Principally, for an individual to become influential a community member needs to make

their presence known through highly active communication (posting and thread

starting) on related topics and use certain terminologies and languages that the rest of

the forum are familiar with. Furthermore, this has illustrated ways for community

members to target those that may have the most influence over others which may aid

administrative roles or indeed those looking for social marketing research in

correspondence with the Two-Step Model of Influence (Katz & Larsfield, 1957).

8.3. Conclusions and Knowledge Contributions

This thesis has demonstrated the fluidity of social identities of influentials

within online communities. Equally, this has demonstrated through qualitative enquiry

the cycle that influential individuals embark upon when entering a new online

community and trying to gain some momentum and the transitional process of social

identity and group membership within these communities (Tajfel & Turner, 1972).

This builds upon existing notions of online identities and illustrates how the social

identity approach is especially relevant to influentials and how their self-categorization

is central to their identity and influence over others in online fora (Champniss et al.,

2015; Maden et al., 2007; Vernuccio et al., 2015; Zhang et al., 2010). As such, this

adds knowledge and demonstrates that individuals follow those who actively endorse

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their social category and even occasionally create prejudice to ‘out-groups’ (Tajfel &

Turner, 1979; Turner, 1982).

There was a clear cycle that influential individuals underwent within these

online communities which provides some valuable contribution towards the literature

regarding how influential individuals subsequently lose their status within communities

(Huffaker, 2010). This is a particularly unique contribution as the use of mixed

methods enabled, not only the identification of individuals, but an in-depth exploration

of the transitional processes of online identities and fluidity of influence over time. This

illustrated online behaviours apparent in the decline of influence.

As exhibited in part two of the analysis (chapter six), individuals often engaged

in repetitive behaviours when their influence was diminishing within their given

communities; while no casual effect can be drawn from this finding it could be a

potential indicator as to why some level of followership was lost. Similarly, individuals

had a decline in online activity within the forum when losing elements of their status;

this was also supported with the qualitative analysis as individuals often remarked at

their lack of activity within the forums towards the end of their community tenure.

Another interesting contribution and finding is the notion that individuals enter

asking questions but ultimately when their influence is diminishing they seem to regress

and once again display a lack of knowledge and begin to ask questions once more. This

is a noteworthy finding with regards to why one may lose their reputation within online

communities and also adds some contribution to the literature on knowledge sharing in

online communities (Childer, 1986; Ventraman; Weimann et al., 2007). Therefore,

gaining attention and asking questions helps individuals find a niche or group within

an online community which had not been publicised with regards to SIT (Tajfel &

Turner, 1979).

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The narrative analysis also revealed the use of mockery within online

communities which has not yet been widely researched. Evidently, individuals often

engaged in mockery towards higher levels of authority when becoming prominent

members of their online community. In addition to this point, it was seen that when

individuals were losing their followership and admirers, other members often mocked

them. As such, this provides a new line of enquiry for online community research with

regards to influence. This illustrates how strong notions of online identities (and quick

wit) can help sustain some level of credibility (Tajfel & Turner, 1979).

With regards to SIT specifically, the in-depth exploration of social interactions

illustrated how group membership and categorisation is central to the identities of these

influential members (Tajfel & Turner, 1972). More specifically, it has illustrated that

when losing status, they disengage from their groups membership – whilst this cannot

determine causality, it does exemplify the need to have a clear social identity congruent

with the online forum to gain momentum and status. Subsequently, influential

individuals lost this notion of collective-identity when their influence was declining.

Finally, individuals that were seen to be losing their influence in their respected

community also appeared to have separated with elements of their social identity,

therefore, opposing previous research which had seen social identities are fixed (Maden

et al., 2007; Zhang et al., 2010). Consequently, this appears to be a noteworthy

contribution to losing status as well as an important finding with regards to influential

individuals in online communities as they need to have a strong sense of belonging and

collective identity to sustain reputable statuses within online communities, which has

been omitted from online identity literature thus far. As such, the social identity

approach has not occupied a role examining influentials in the existing research, which

this research has contributed.

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In summary, this research has made the following contribution to literature and

knowledge. Firstly, this research has utilised a mixed methods approach to studying

online identities of influential community members. Therefore, the first empirical stage

identified those influential and then the qualitative phase examined the online

behaviours and characteristics of influential individuals which can distinguish them

from their followers. Moreover, previous research examining the social identity

approach online has often focused on virtual teams, which in nature are task orientated

and purpose-created (Kimble, 2011; Sivunen, 2006; Vahtera, Buckley, Aliyev, Clegg,

& Cross, 2017; Wei-Au, 2010). Therefore, previous research suffers limitations as SIT

and virtual teams appears to be reductionist in the sense that these groups have existing

in-group and out-groups oppositions and existing prejudice (Coupland, 2010; Van De

Mieroop, 2015; Widdecombe, 1998). Yet, this research in comparison, adds to the

theoretical platform by utilising an naturally occurring, longitudinal data for

examination and investigation into the conversation as well as the online identities of

influential users.

8.4. Summary of Key Limitations

The first major limitation to the research is the fact the researcher used

secondary data analysis of existing data for the initial stage of the empirical research.

Equally, the research incorporated a small (but in depth) sample. However, the data

that was used was significant within the data base and the in-depth qualitative analysis

has added some sense of triangulation. Equally, threads opposed to posts were

examined. This was due to numerous time constraints on the research as well as the

issue that posts pose with coherency and loss of context. Furthermore, only two online

communities were utlised in this research. This also acts a limitation to the research in

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that they were rather different in regards to the philosophical foundations. In other

words, one forum was religious and the other forum was political.

8.5. Summary of Practical Implications

This research has numerous practical implications which are as follows. This

research has highlighted numerous ways in which influentials could potentially be

disrupted within an online community. This has revealed ways in which an online

community member can augment their influence amongst their peers; but, more

importantly, ways in which their following can be diminished which has not yet been

published.

This research could therefore be ulitised within social marketing research, to

illustrate ways in which particular brands could flourish or fail. Additionally, this

research has presented ways to gain influence in online communities which could be

utilised in counter-terrorism policy.

8.6. Summary of Future Recommendations for Research

Future research needs to examine different online communities with different

ideological foundations to assess whether similar findings apply. Equally, this research

did not examine attachment styles of online influentials and other community members

within different online forums, which could be of further interest to researchers in this

area.

Making amends from the limitations of this research, future research should

utilise a larger sample size for mixed methods investigations into online communities.

Finally, research needs to experimentally verify some of the findings from the empirical

chapters. Therefore, research needs to advance the area further by investigating

whether undermining tactics and mockery does indeed reduce influence and credibility

to onlookers in online forums.

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8.7. Summary of Gaining/Losing Influence in Online Communities

This purpose and unique contribution of this thesis was to illustrate how

individual members gain and lose status within their on line communities. Utilising a

combination of mixed-method on naturally occurring longitudinal data this thesis has

illustrated ways in which one may gain status when joining a new online forum. By

combining the empirical findings, an individual needs to have regular activity (posting

and thread starting) behaviour when joining a new forum. Equally, online users must

attract attention to their potential followership. This as seen in the narrative analysis

can be achieved in a number of ways.

1. Discussing popular topics

2. Undermining/mocking popular individuals

3. Finding a role

In addition, aspiring influentials need to create some intergroup bias via an “in-group”

and “out-group”. Also, techniques apparent in the narrative analysis when individuals

were gaining some status was the use of powerful and emotive language and the

creation of a common goal for the group. Finally, to become an influential online

community member one must identify and self-categorise as a group-member.

Accordingly, this research has also shed light on a newer, more contemporary

issue; how do people lose influence? Indeed, this particular question and research

finding has more real world applicability and a stronger contribution to knowledge.

Whilst some of these may seem obvious, this is the first research to not only

qualitatively explore identification markers of individuals and illustrate how influence

is gained amongst followers, but this research also shows online behaviours when

influentials were losing their status. Therefore, this research has illustrated that there

is a clear decrease in activity when losing influence. This decline is not only with

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regards to posting and thread-starting behaviour but also with the interactions with

others in the online forum.

In addition, these individual use less convincing language when losing their

status, they ask more unassertive questions, they question the beliefs of the collective-

identity. Equally, they display far less knowledge about current issues and generally

appear uniformed in group matters. Another interesting finding is that individuals who

lose their influential status appear to undermine the effort of the group or the group

goals. In some cases, individuals even begin to question themselves.

Accordingly, there also appears to be a unique findings in that these individuals

tend to get ‘mocked’ or ‘undermined’ by others, and have less witty or quick comebacks

to defend themselves. Finally, and perhaps not surprising, this individuals break from

their once well-establish social identities.

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CHAPTER NINE

REFLECTION ON MY JOURNEY

If a PhD was a test of shear endurance I would be a sure winner. I have never

had so many ups and down throughout my life than conducting and subsequently trying

to convey meaning from this research. I have reflected in my introductory chapter a

little on each of the various chapters, so this reflection chapter is mainly focused on the

thesis and PhD process as a whole.

When I look back at the idea I started with and the final product – I notice that

these are worlds apart. Equally, I have at times, found it very difficult to let go of my

initial ideas and progress with the research process. However, I feel that having a

supervisor that I have been able to call upon in times of distress has made this process

somewhat easier for me. Moreover, having taken on board comments from the viva, I

must admit I was grateful to see the removal of theory on leadership. Coming from a

social psychology background, social identity is something I am far more fond of and

find more applicable to my research and the world in general, due to my own personal

interests in the topic.

I had found leadership a very broad area and often confusing leadership and

influence had left my research and brain in somewhat of a mental-tangle. I do feel that

incorporating my revised theoretical framework of Social Identity Theory has provided

some much needed clarity to my work. I believe this was what I had initially wanted

to bring out in my qualitative chapter but through various changes and pressures from

external funders, and others interested in my research, my desires and focus point got

lost somewhere along the line. In addition, whilst making amendments, the world

changed, which was unhelpful. The past year has seen a global pandemic due to

COVID-19. This virus closed schools, shops, workplaces and created a new digital era

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where even older generations had to adapt and seek digital platforms to communicate

with family and friends.

In light of the numerous lockdowns that the UK, in particular has seen, my

progression stopped; thus, my amendments stopped. Therefore, picking up from where

I last left off with amendments and various changes was extremely difficult, especially

whilst home-schooling and occupying my daughter. However, the importance of this

research has come to light and the need for exploration into online communities and

identities is now a prominent issue in light of businesses, individuals, shopping and

school, all entering a new, virtual reality, from which the world may never be the same.

As part of professional practice I have been made aware of the importance of

reflective practice, and as such, I have decided that this chapter is as important as the

research because it documents the research process and acknowledges my journey as a

researcher and illustrates the making of this thesis. My particular journey and learning

curve has encountered numerous obstacles and issues. Not purely based on the

difficulties that PhD students encounter when embarking on this research process, but

myself and my personal circumstance, which has continually been a major bump in the

road.

My learning curve has been extremely difficult, while I was not necessarily

naive in the notion of completing a thesis and all the hard work that would go into this

process, but more, the affect this has on life and family. Whilst still collecting my data

I fell pregnant with my daughter and juggling family life single-handedly and trying to

get a doctorate has been my greatest life challenge thus far. Equally, I did not

completely understand the process of a PhD and how part of this process is the ability

to change direction and adapt; thus, the feeling of losing control and doubting in my

own abilities has been a difficult element to overcome over the past few years.

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Initially, I had started at an entirely different University, then moved when my

then supervisor and his team got a promotion at a different University. So the thesis

seems to have changed direction many times and taken far longer than projected. Also,

the recent global pandemic has seen many school closures and numerous lockdowns,

which has also effected, not only the way online communities are used now, but

impacted on my ability to do work whilst entertaining and home-schooling my

daughter.

9.1. Literature Review

While I have previously always found literature reviews an enjoyable part of

the research procedure, I have found this one of the most difficult elements. I believe

this was large due to the change in direction, when I changed supervisory teams and

began using influence and leadership interchangeably within the thesis. Equally, as

this had been a funded project there was some pressure from funders as I was expected

to undermining credibility as a main focal point. However, the focal point of this thesis

has since changed from credibility and it was decided that the undermining credibility

experimental element to become a separate paper (which it now is) and removed from

this thesis. As such, I found framing the research was extremely difficult and the work

did seemed to lack some coherence and structure in the literature review.

In other words, at the very beginning of this research, I had planned to have

three parts to this research; a qualitative aspects, MySQL aspect (a fourth generation

database) and finally an experimental chapter where I was to undermine credibility.

However, all the pieces did lack some coherence and in my progress review I was given

some advice to take the final chapter out as it did not seem to “mesh” as well as the

other two aspects. I took this advice, but the main aspect of the research and the bit

that the funders had most wanted, was the experimental chapter. Therefore, the whole

161

focus of my PhD changed as I was no longer trying to frame that last part of the thesis

in my literature review, then my work seemed to get a little more coherent. I was no

longer trying to discuss online communities, influence, leadership and the importance

of undermining credibility (with regards to the experimental chapter). Accordingly,

this then gave me some clarity with regards to the literature search.

Nonetheless, there was also some confusion with regards to incorporating

leadership and beginning to use influence and leadership interchangeably (as some

research has done in my literature review). However, I had frequently struggled with

this, and in complete truth, I never found the literature on leadership to necessarily fit

with my empirical chapters. This was discussed at long length with the examiners and

I did show far less passion and understanding of this broad area in comparison with the

influential literature. Consequently, it was decided to remove leadership from the thesis

– a decision I wholeheartedly supported. Indeed, as mentioned, some other research

does tend to use these two terms (influence and leadership) interchangeably, which is

evident in the literature review and if the authors on the papers that I am discussing

refers to online communities members as leaders, I do so. However, I, myself, the

researcher, have decided to remove this element and focus on influentials – as I am not

entirely certain whether leadership or leader in the managerial sense, occupies a place

in online communities.

When removing leadership from the thesis, there was a clear lack of a theoretical

framework. Another huge obstacle that I encountered. I had been given several

theoretical platforms to explore such as group dynamics, influence or reputation, to

name a few. But, felt that, perhaps due to the nature of online communities, these did

not necessarily inhabit a role either. For example, when exploring group dynamics with

virtual teams, group dynamics proclaims stages of group formation (Bonebright, 2010;

162

Cassidy, 2007; Colombini & McBridge, 2012; Hogg, 1987; Tuckman, 1964; 1965).

Within online communities, there is not necessarily a task; as such, group dynamics did

not necessarily apply to both forums the I examined (not to say all online communities

– just ones that were utilised in this research), more so virtual teams.

Taking the previous notion on board, I noticed that individuals did have a strong

sense of identities within these online communities – something that I had initially

explored but lost amongst the leadership research. As such, a social identity approach

provided a greater understanding on the online identities of influentials – especially

with both communities having strong ideologies. Having come from a social

psychology background – I noticed that this was evident in the qualitative chapters

which had often been overlooked in comparison to the quantitative element of the

thesis. Consequently, Social Identity Theory (SIT; Tajfel & Turner, 1979) does provide

a stronger sense of theoretical clarity and in-depth exploration into online identities of

influentials, which was previously omitted. Therefore, this theoretical clarity

compliments my quantitative chapter as it identifies influentials. These identities of

these influentials are then explored over time and tenure within the community.

Consequently, this thesis appears to advocate the premise that social identities are not

fixed identities and are fluid and can change over time.

9.2. Methodology

With regards to the methodology, there were no huge challenges for me

personally except for the obvious issue with three phases becoming two. As well as

the final phase of empirical research being taken out it was suggested to re-arrange the

two phases. Therefore, what is now known as phase one (SQL and quantitative

research) was in fact originally phase two and vice-versa. However, there was more

163

fluidity to the research changing these two bits around in the empirical chapters and I

fully agreed with the change and felt like it was a good direction for my thesis.

Therefore, I felt slightly more conflicted and struggled to decide whether a

critical realism stance may have been more appropriate for my methodology; however,

my initial journey did start with a qualitative exploration, which gradually expanded

thus, rationalising my choice to adhere to the pragmatic ontology. However, actually

conducting the research did pose many different challenges which I will discuss in the

empirical reflection of this chapter.

I did not gather the role transition data myself, this was secondary data, which

I modified and utilised in my research. It was given to me by my supervisor when I

first embarked upon the PhD and told that it was data which he has been working on

that the funders had wanted to see more of this. This was given to me in a MySQL

database file (known as a dump) and contained records of all the users, posts, threads,

reputation score, reputation power, seniority, number of thanks and whether they were

categorised as a ‘leader’ (which is an influential online community member for the

purpose of this research) over a two year period. I was given a ‘dump’ for each of the

two communities I discussed throughout my thesis.

While I did not gather the meta-data and compile it into a database, the

remaining coding and retrieving of information was entirely designed by myself.

Therefore, I had to learn how to navigate the MySQL database to retrieved the

information and also create my own data tables. This is where I decided that users that

had been classed as ‘leaders’ for two years were then categorised into Group 1 (Always

PP), Group 2 were those that lost status and went to a lesser role (PP Demoted), Group

3 were those that were a lesser role and then promoted to popular status (Promoted PP)

and finally Group 4 those that were popular then became inactive members (PP

164

Inactive). I had to extract these groups and then code then into MySQL to retrieve the

necessary data for the groups and individual users.

I am not particularly computer savvy; however, my first supervisor was.

Therefore, this was something that I had to learn very quickly and recruit friends who

could help in this particular area. I can easily state that learning this fourth generation

language was extremely difficult as it had to be typed perfectly for the commands to

work which I spent many hours pulling my hair out over. Though, I found completing

a coding booklet of the various queries I used and keeping track of my commands was

the easiest way to extract the correct information. Please see below for an example of

some of the commands used throughout the role transition chapter:

For individual data retrieval:

To select threads and word count query used:

select counT(postnum), sum(postwordcount),date_format(time,'%Y%m') as

NewTime from rev_left.all_revleft

WHERE poster LIKE "%Fawkes%" and postnum = 1

GROUP by date_format(time,'%Y%m')

order by date_format(time, '%y%m')

To select by year (With missing numthanks values-1):

select counT(postnum), sum(postwordcount),date_format(time,'%Y'),

sum(numthanks) from rev_left.all_revleft

WHERE poster LIKE "%black magick%" and numthanks != -1

GROUP by date_format(time,'%Y')

order by date_format(time, '%y')

To select group data:

select sum(numthanks), avg(numthanks) from rev_left.all_revleft where

`status` = 5 and numthanks != -1

To select group data for MANOVA:

165

select counT(postnum), sum(postwordcount), date_format(time,'%Y%m') as

NewTime, numthanks from rev_left.all_revleft

WHERE `status` = 5

group by numthanks

order by date_format(time,'%Y%m');

This is an example of some of the various queries that were used to collect the

information that was then transferred onto SPSS for analysis. I also feel especially

proud when I look at this chapter as before this I had regarded myself as a qualitative

researcher; thus, I really left my comfort zone and pushed myself with the various

models and statistical analyses for this aspect of the empirical research which I am now

most pleased with.

Accordingly, the second phase of the research was something that I thoroughly

enjoyed which I think is evident in my writing. While sometimes I do have a tendency

to over-interpret, years of completing qualitative projects have in some ways managed

to limit my tendency to do this. Equally, I had two online communities to improve my

interpretations as Left Wing Politics6 (LWP) and Islamic United (IU) did have similar

findings, which provides, to some extent, qualitative rigor to my analysis and

interpretations. Furthermore, all interpretations and analysis has been checked via

numerous researchers involved in the thesis as part of my doctoral team to improve the

credibility of the findings.

In addition to the latter point, I always use Braun and Clarke’s (2006)

framework when collecting and gathering data for qualitative analysis. Despite the

analysis being labeled narrative analysis (discussed later), I feel that this framework

documents well the entirety of the qualitative process. Also, as narrative analysis is a

rather flexible process, including a thematic edge does provide some consistency and

6 Pseudonyms for the communities

166

improves the audit trail of my research. Essentially, this involved reading all the threads

and corresponding comments on threads of specific individuals (16 used for qualitative

analysis) and copying and pasting any interesting threads onto a word document. Each

words document was the reviewed and coded accordingly, making notes where

applicable. Once all the online community members had been examined, I go through

each users’ and make a tally chart of all the comments to observe emerging themes, the

most prominent tallies then become the themes. Indeed, this almost quantifiable way

of doing thematic analysis enabled me to see my emerging themes more clearly and

removes any elements of bias that I, as the researcher, may have had.

I have always found qualitative research extremely rewarding and enjoyable. I

had struggled with various difficulties in respect to which analysis I was actually doing

in the beginning. Having previously toyed with the idea of doing standard thematic

analysis I then decided that it was more a narrative analysis as a lot of the existent

literature on online communities seemed to be following the thematic guideline but

denoting that it was narrative due to the online nature; as such, I followed suit. Equally,

this method of analysis appeared to allow more depth into language used which made

it more appropriate for online community research and enabled me to examine the

context of the conversations between users.

9.3.Study One - Quantitative Examination of Role Transitions

The role transition chapter was initially only conducted on the 16 users that were

discussed in the qualitative chapter. I then decided that seen as I had the data available,

to examine this in further detail. Therefore, global community statistics were examined

and I then categorised the users into the four groups as previously mentioned for further

analysis. This was to improve the reliability and generalisability of the research. In

doing this, I found myself finding further elements to examine and decided to conduct

167

further analysis into social network analysis and then went onto to various models for

the data. Equally, I then cross-compared any differences between the two communities

as I felt this was a fitting end to the chapter as they were constantly compared.

Similarly, I also felt that this improved the research as the qualitative aspect only

focused on threads (and subsequent posts or comments) but this examined a large

amount of data to validate my research and findings and identified influential members

to explore in further depth.

Although the database was secondary data which had already been time sliced

to illustrated various different roles, I feel that educating myself on this database and

subsequently determining my own categories for analysis meant that I have achieved a

good understanding of the data and thus, this diminishes some of the issues around

utilising secondary data. However, while the data for the role transition was indeed

secondary data, the second phase of the research was collected and analysed by myself.

Moreover, the role transitions only examined individuals that move over a two-

year period. However, the global statistics examined the entire community lifespan up

until the data was collected. I thought it would improve the scientific rigor of my

research to include both of these aspects; therefore, improving the validation of this

phase of the research. Therefore, the main aim of this chapter is to identify influentials

over time. Then, the qualitative chapter can explore online identities and the

transitional process of becoming influential in far more depth. This adds knowledge to

SIT (Tajfel & Turner, 1979) by examining the identities of those influential individuals,

and exploring how their group membership and social category is embedded in their

influential status (and loss of status).

168

9.4. Study Two – Narrative Analysis of Influential Individuals

Phase two of the research was the qualitative aspect, which as discussed was

collected and analysed via narrative analysis but with a strong thematic edge. This

particular chapter underwent numerous difficulties and adjustments throughout the PhD

journey. Firstly, I had only intended to examine those that rose to influential status at

the beginning of my PhD journey but as I investigated further it seemed fitting to

analyse the flip side of gaining influence; losing status. Furthermore, this appeared to

be a caveat in the literature and the tactic that had the most real world applicability.

Accordingly, I included another four users from each community that had

subsequently lost their influence amongst their followers and been demoted to a lesser

role. As such, each community then had eight users (16 in total) four that rose to status

and four that lost status. Initially, these had been segregated into community chapters

but then merged together into the chapters they are at present; rising to status and losing

status.

I do feel that in some parts there is some repetition to the chapters having them

as a rise to status and lose status chapters accordingly; however, this does improve the

dependability of the research as findings do seem to be consistent in parts across the

communities (despite the very different philosophical underpinnings). While there is

some repetition it does show a similar journey that individuals embark on when entering

a forum and gaining some momentum. However, any repetition from the ‘gaining

status’ chapter that I had included in the ‘losing status’ chapter was removed as it had

been described as ‘confusing’ in parts.

Whilst the first phase only shows time-sliced data from the 16 individual users

over a two-year period, the second phase of the research illustrates their entire

community lifespan to substantiate any claims that were made throughout the various

169

analyses. This does form some verification for the findings derived much like data

triangulation. Equally, examining the entire community lifespan provides a dialog and

an essence of communication that may have been lost without qualitative exploration

throughout the whole lifespan. This also allowed me to exemplify how the language,

interactions and social identities changed over the course of time.

Furthermore, as previously mentioned, despite the qualitative analysis only

utilising threads from users, posts were also examined when necessary, as a form of

member checking or cross validation and all corresponding information such as

comments were examined with each thread to illustrate the naturally occurring

conversations in the online environment. In addition to this point, the first phase of the

research examined all different behaviours; thus, providing a holistic image of each of

the different community members that were examined in both aspects of the research.

Therefore, while this may seem like a limitation it was the most viable way to extract

information from a substantial amount of data from each of the forums; also, posts can

often be deleted thus omitting crucial dialog. This chapter is rather long; therefore, I

decided to create a summary table (at the advice of others) to illustrate the main findings

and elucidate the narrative of the theoretical framework, which is most applicable to

this chapter in particular.

9.5. Discussion

The discussion was one of the hardest PhD challenges to overcome. I believe

that at some point I developed what is known as writers block. This is the inability to

write anything and I suffered with constant ‘mind-blank’. Usually, I find discussions

quite enjoyable as they are an opportunity to document the limitations you have found

but also the main analytical contributions that the research has made. However, I

believe that my daughter hit the ‘terrible twos’ phase by the time I got round to

170

completing this chapter which had an impact on my ability to work and concentrate for

long periods of time.

Having initially thought that juggling all my commitments would be a doddle

this chapter was the chance to finally pull together all the work that I had done but I

struggled greatly to see the thesis as a whole piece of work and unite it in this chapter

to complete a full draft. To overcome this I had to go to boot camp where I was once

again told to stop doubting myself and begin to piece together the thesis and ‘just type’.

Whilst I have been careful to prevent over-interpretations, I do think that it

should be noted that my socio-economic status would have had some influence of my

interpretation of the data. I do not hold any political views and have always maintained

that I do not advocate any political stance, which may have both helped and hindered

my research. Indeed, someone who identifies with a political group may have

interpreted the findings and threads that I examined differently. In addition, I do not

practice the pillars of Islamic faith. Similarly, I do not speak Arabic but have often

asked those who are fluent in Arabic to cross check and validate my interpretations that

I have translated. I feel that having this neutrality allowed me to remain relatively bias

free (as much as possible) and try and see the themes and data as they were intended

(though obviously I cannot be certain of this).

To establish trustworthiness in this research it is vital to convey the ways in

which I; as a researcher, may have influenced this investigation. Indeed, while it is

impossible to remain completely bias free, it is possible to control my existing

preconceptions on this matter. As such, my aim throughout this particular chapter was

to demonstrate continuous reflexivity and self-scrutiny through the recognition of my

role within this investigation, which may have inadvertently influenced the research

process and explain any choices that I made with regards to this thesis which may seem

171

unusual to those whom have not been actively involved in the research process.

Without this documentation, the trustworthiness of the present report cannot be

ascertained.

172

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APPENDICES

Appendix 1: Ethical Approval

RESTRICTED ACESS

206

Appendix 2: MySQL Queries

FOR INDIVIDUAL DATA

TO SELECT THREADS AND W/C

select counT(postnum), sum(postwordcount),date_format(time,'%Y%m') as

NewTime from rev_left.all_revleft

WHERE poster LIKE "%Fawkes%" and postnum = 1

GROUP by date_format(time,'%Y%m')

order by date_format(time, '%y%m')

TO SELECT BY YEAR (WITH MISSING NUMTHANKS VALUES -1)

select counT(postnum), sum(postwordcount),date_format(time,'%Y'),

sum(numthanks) from rev_left.all_revleft

WHERE poster LIKE "%black magick%" and numthanks != -1

GROUP by date_format(time,'%Y')

order by date_format(time, '%y')

TO SELECT BY YEAR - NUMTHANKS, W/C , POST TOTAL

select counT(postnum), sum(postwordcount),date_format(time,'%Y'),

sum(numthanks) from rev_left.all_revleft

WHERE poster LIKE "%black magick%"

GROUP by date_format(time,'%Y')

order by date_format(time, '%y')

TO SELECT GROUP DATA STUFF

select sum(numthanks), avg(numthanks) from rev_left.all_revleft where `status` = 5

and numthanks != -1

TO SELECT GROUP DATA MANOVA

select counT(postnum), sum(postwordcount), date_format(time,'%Y%m') as

NewTime, numthanks from rev_left.all_revleft

WHERE `status` = 5

group by numthanks

order by date_format(time,'%Y%m');

select seniority, reputation from rev_left.all_revleft

where `status` = 1

207

group by poster

order by poster

QUERY FOR MANOVA SPSS:

SELECT poster, seniority, sum(postwordcount), reputation, sum(numthanks)

FROM `rev_left`.`all_revleft` where `status` = 3 and numthanks != -1

group by poster

QUERY FOR CREATING COLUMN

SELECT

*

FROM

roles_timeliced.rl_roles_timesliced

where

`id` in ( select `poster` from rev_left.all_revleft )

;

UPDATE

rev_left.all_revleft

SET

`status` = 10

WHERE

poster like '%Barry Lyndon%' ;

QUERY FOR DATABASE

select

poster,

counT(postnum) as numberOfPosts,

sum(postwordcount) as wordcount,

date_format(time,'%Y-%m'),

sum(numthanks > 0),

sum(postnum=1) as threads,

sum( IF( postnum = 1, postwordcount, 0 ) ) as threadwordcount

from

rev_left.all_revleft

Where

date_format(time,'%Y-%m') > '2009-11'

and

208

`status` = 1

GROUP by date_format(time,'%Y-%m-%d')

order by poster

QUERY FOR MULTI-LEVEL MODELS SPSS

select count(postnum), reputation, poster, sum( IF( postnum = 1, postnum, 0 ) ) as

threadwordcount, sum(numthanks > 0) from rev_left.all_revleft

Where

date_format(time,'%Y-%m') > '2009-11'

and

`status` = 1

group by poster

order by poster

209

Appendix 3: Sample of SPSS Output for Phase One

MANOVA & MLM LWP

Between-Subjects Factors

Value Label N

Group Status 1 Always PP 43

2 PP - Another Role 29

3 PP - Inactive 19

4 Promoted - PP 16

Descriptive Statistics

Group Status Mean Std. Deviation N

Total Number of Posts Always PP 4077.40 2400.971 43

PP - Another Role 2088.48 2656.973 29

PP - Inactive 3800.58 5530.725 19

Promoted - PP 2288.19 2639.625 16

Total 3221.64 3342.033 107

Word Count for Posts Always PP 249215.93 222554.996 43

PP - Another Role 96116.79 93926.351 29

PP - Inactive 197185.32 366635.303 19

Promoted - PP 96044.94 132860.645 16

Total 175578.62 228572.949 107

Reputation Always PP 3705.33 2369.196 43

PP - Another Role 1032.72 831.390 29

PP - Inactive 767.58 2714.287 19

Promoted - PP 520.75 547.214 16

Total 1983.12 2395.415 107

Number fo Thanks Received Always PP 1468.56 1148.123 43

PP - Another Role 324.10 285.366 29

PP - Inactive 468.89 531.710 19

Promoted - PP 446.31 320.123 16

Total 828.01 941.988 107

Thread Word Count Always PP 12369.81 13413.554 43

PP - Another Role 5714.55 8145.960 29

PP - Inactive 14464.89 22003.684 19

Promoted - PP 5373.56 6895.209 16

210

Total 9891.91 13851.267 107

Thread Count Always PP 60.79 66.206 43

PP - Another Role 24.90 27.817 29

PP - Inactive 49.63 60.390 19

Promoted - PP 26.94 36.450 16

Total 44.02 54.884 107

av_numthanks Always PP 3.9824 3.44720 43

PP - Another Role 10.1248 18.33318 29

PP - Inactive 22.3714 44.01906 19

Promoted - PP 11.2603 23.30610 16

Total 10.0008 23.27579 107

Average Thread Word Count Always PP 252.1730 269.97332 43

PP - Another Role 345.5033 854.85891 29

PP - Inactive 277.4852 236.35806 19

Promoted - PP 313.9647 522.35140 16

Total 291.2027 521.10508 107

Average Post Word Count Always PP 62.6250 42.98549 43

PP - Another Role 55.4558 35.84885 29

PP - Inactive 51.5646 28.10025 19

Promoted - PP 55.6137 54.84119 16

Total 57.6695 40.61902 107

Box's Test of Equality of

Covariance Matricesa

Box's M 843.036

F 5.042

df1 135

df2 10475.674

Sig. .000

Tests the null hypothesis

that the observed

covariance matrices of the

dependent variables are

equal across groups.

a. Design: Intercept + Status

Multivariate Testsa

211

Effect Value F

Hypothesis

df Error df Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerd

Intercept Pillai's

Trace .830 51.621b 9.000 95.000 .000 .830 464.586 1.000

Wilks'

Lambda .170 51.621b 9.000 95.000 .000 .830 464.586 1.000

Hotelling's

Trace 4.890 51.621b 9.000 95.000 .000 .830 464.586 1.000

Roy's

Largest

Root

4.890 51.621b 9.000 95.000 .000 .830 464.586 1.000

Status Pillai's

Trace .625 2.835 27.000 291.000 .000 .208 76.534 1.000

Wilks'

Lambda .438 3.366 27.000 278.091 .000 .241 88.138 1.000

Hotelling's

Trace 1.143 3.965 27.000 281.000 .000 .276 107.048 1.000

Roy's

Largest

Root

1.006 10.841c 9.000 97.000 .000 .501 97.572 1.000

a. Design: Intercept + Status

b. Exact statistic

c. The statistic is an upper bound on F that yields a lower bound on the significance level.

d. Computed using alpha =

Levene's Test of Equality of Error Variancesa

F df1 df2 Sig.

Total Number of Posts 5.710 3 103 .001

Word Count for Posts 3.559 3 103 .017

Reputation 7.041 3 103 .000

Number fo Thanks Received 8.729 3 103 .000

Thread Word Count 6.454 3 103 .000

Thread Count 4.003 3 103 .010

av_numthanks 9.263 3 103 .000

Average Thread Word

Count 1.068 3 103 .366

Average Post Word Count 1.851 3 103 .143

212

Tests the null hypothesis that the error variance of the dependent variable is equal across groups.

a. Design: Intercept + Status

Multivariate Testsa

Effect Value F

Hypothesis

df Error df Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerd

Intercept Pillai's

Trace .830 51.621b 9.000 95.000 .000 .830 464.586 1.000

Wilks'

Lambda .170 51.621b 9.000 95.000 .000 .830 464.586 1.000

Hotelling's

Trace 4.890 51.621b 9.000 95.000 .000 .830 464.586 1.000

Roy's

Largest

Root

4.890 51.621b 9.000 95.000 .000 .830 464.586 1.000

Status Pillai's

Trace .625 2.835 27.000 291.000 .000 .208 76.534 1.000

Wilks'

Lambda .438 3.366 27.000 278.091 .000 .241 88.138 1.000

Hotelling's

Trace 1.143 3.965 27.000 281.000 .000 .276 107.048 1.000

Roy's

Largest

Root

1.006 10.841c 9.000 97.000 .000 .501 97.572 1.000

a. Design: Intercept + Status

b. Exact statistic

c. The statistic is an upper bound on F that yields a lower bound on the significance level.

d. Computed using alpha =

Parameter Estimates

Dependent

Variable

Parame

ter B

Std.

Error t

Si

g.

95% Confidence

Interval

Partial

Eta

Squar

ed

Noncen

t.

Parame

ter

Observ

ed

Powerb

Lower

Bound

Upper

Bound

Interce

pt

2288.18

7 815.095

2.8

07

.00

6 671.640

3904.73

5 .071 2.807 .794

213

Total

Number of

Posts

[Status

=1]

1789.20

8 954.772

1.8

74

.06

4 -104.358

3682.77

3 .033 1.874 .459

[Status

=2] -199.705 1015.34

9

-

.19

7

.84

4

-

2213.41

0

1814.00

0 .000 .197 .054

[Status

=3]

1512.39

1

1106.28

0

1.3

67

.17

5 -681.655

3706.43

8 .018 1.367 .273

[Status

=4] 0a . . . . . . . .

Word

Count for

Posts

Interce

pt 96044.9

38

55145.8

58

1.7

42

.08

5

-

13323.8

66

205413.

741 .029 1.742 .407

[Status

=1]

153170.

993

64595.8

59

2.3

71

.02

0

25060.3

42

281281.

644 .052 2.371 .651

[Status

=2] 71.856 68694.2

14

.00

1

.99

9

-

136166.

916

136310.

627 .000 .001 .050

[Status

=3] 101140.

378

74846.2

63

1.3

51

.18

0

-

47299.5

32

249580.

288 .017 1.351 .268

[Status

=4] 0a . . . . . . . .

Reputation Interce

pt 520.750 487.842

1.0

67

.28

8 -446.770

1488.27

0 .011 1.067 .185

[Status

=1]

3184.57

6 571.441

5.5

73

.00

0

2051.25

8

4317.89

4 .232 5.573 1.000

[Status

=2] 511.974 607.696

.84

2

.40

1 -693.248

1717.19

7 .007 .842 .133

[Status

=3] 246.829 662.120 .37

3

.71

0

-

1066.33

0

1559.98

8 .001 .373 .066

[Status

=4] 0a . . . . . . . .

Number fo

Thanks

Received

Interce

pt 446.312 197.481

2.2

60

.02

6 54.655 837.970 .047 2.260 .610

[Status

=1]

1022.24

6 231.322

4.4

19

.00

0 563.472

1481.01

9 .159 4.419 .992

[Status

=2] -122.209 245.999

-

.49

7

.62

0 -610.090 365.672 .002 .497 .078

[Status

=3] 22.582 268.030

.08

4

.93

3 -508.991 554.156 .000 .084 .051

214

[Status

=4] 0a . . . . . . . .

Thread

Word

Count

Interce

pt 5373.56

2

3381.38

5

1.5

89

.11

5

-

1332.61

8

12079.7

43 .024 1.589 .350

[Status

=1]

6996.25

1

3960.83

2

1.7

66

.08

0 -859.125

14851.6

28 .029 1.766 .417

[Status

=2] 340.989 4212.13

1

.08

1

.93

6

-

8012.78

0

8694.75

9 .000 .081 .051

[Status

=3]

9091.33

2

4589.35

7

1.9

81

.05

0 -10.576

18193.2

40 .037 1.981 .501

[Status

=4] 0a . . . . . . . .

Thread

Count

Interce

pt 26.937 13.296

2.0

26

.04

5 .568 53.307 .038 2.026 .519

[Status

=1] 33.853 15.574

2.1

74

.03

2 2.965 64.741 .044 2.174 .577

[Status

=2] -2.041 16.562

-

.12

3

.90

2 -34.889 30.807 .000 .123 .052

[Status

=3] 22.694 18.046

1.2

58

.21

1 -13.095 58.483 .015 1.258 .238

[Status

=4] 0a . . . . . . . .

av_numtha

nks

Interce

pt 11.260 5.668

1.9

87

.05

0 .020 22.501 .037 1.987 .503

[Status

=1] -7.278 6.639

-

1.0

96

.27

6 -20.444 5.889 .012 1.096 .192

[Status

=2] -1.135 7.060

-

.16

1

.87

3 -15.137 12.866 .000 .161 .053

[Status

=3] 11.111 7.692

1.4

44

.15

2 -4.145 26.367 .020 1.444 .299

[Status

=4] 0a . . . . . . . .

Average

Thread

Word

Count

Interce

pt 313.965 131.785

2.3

82

.01

9 52.599 575.330 .052 2.382 .655

[Status

=1] -61.792 154.369

-

.40

0

.69

0 -367.946 244.362 .002 .400 .068

215

[Status

=2] 31.539 164.163

.19

2

.84

8 -294.040 357.117 .000 .192 .054

[Status

=3] -36.479 178.865

-

.20

4

.83

9 -391.215 318.257 .000 .204 .055

[Status

=4] 0a . . . . . . . .

Average

Post Word

Count

Interce

pt 55.614 10.243

5.4

29

.00

0 35.299 75.929 .223 5.429 1.000

[Status

=1] 7.011 11.999

.58

4

.56

0 -16.785 30.808 .003 .584 .089

[Status

=2] -.158 12.760

-

.01

2

.99

0 -25.464 25.148 .000 .012 .050

[Status

=3] -4.049 13.903

-

.29

1

.77

1 -31.622 23.524 .001 .291 .060

[Status

=4] 0a . . . . . . . .

a. This parameter is set to zero because it is redundant.

b. Computed using alpha =

Group Status

Dependent Variable Group Status Mean Std. Error

95% Confidence Interval

Lower

Bound

Upper

Bound

Total Number of Posts Always PP 4077.395 497.203 3091.310 5063.480

PP - Another

Role 2088.483 605.437 887.741 3289.224

PP - Inactive 3800.579 747.982 2317.133 5284.025

Promoted - PP 2288.187 815.095 671.640 3904.735

Word Count for Posts Always PP 249215.930 33638.658 182501.586 315930.275

PP - Another

Role 96116.793 40961.315 14879.685 177353.901

PP - Inactive 197185.316 50605.310 96821.619 297549.013

Promoted - PP 96044.938 55145.858 -13323.866 205413.741

Reputation Always PP 3705.326 297.581 3115.144 4295.507

PP - Another

Role 1032.724 362.360 314.068 1751.380

216

PP - Inactive 767.579 447.675 -120.278 1655.436

Promoted - PP 520.750 487.842 -446.770 1488.270

Number fo Thanks

Received

Always PP 1468.558 120.462 1229.649 1707.467

PP - Another

Role 324.103 146.685 33.188 615.019

PP - Inactive 468.895 181.221 109.485 828.304

Promoted - PP 446.312 197.481 54.655 837.970

Thread Word Count Always PP 12369.814 2062.626 8279.082 16460.546

PP - Another

Role 5714.552 2511.630 733.326 10695.777

PP - Inactive 14464.895 3102.972 8310.882 20618.908

Promoted - PP 5373.562 3381.385 -1332.618 12079.743

Thread Count Always PP 60.791 8.110 44.706 76.876

PP - Another

Role 24.897 9.876 5.310 44.483

PP - Inactive 49.632 12.201 25.434 73.830

Promoted - PP 26.937 13.296 .568 53.307

av_numthanks Always PP 3.982 3.457 -2.874 10.839

PP - Another

Role 10.125 4.210 1.776 18.474

PP - Inactive 22.371 5.201 12.057 32.686

Promoted - PP 11.260 5.668 .020 22.501

Average Thread Word

Count

Always PP 252.173 80.388 92.742 411.604

PP - Another

Role 345.503 97.888 151.366 539.641

PP - Inactive 277.485 120.935 37.640 517.331

Promoted - PP 313.965 131.785 52.599 575.330

Average Post Word Count Always PP 62.625 6.248 50.233 75.017

PP - Another

Role 55.456 7.609 40.366 70.546

PP - Inactive 51.565 9.400 32.922 70.207

Promoted - PP 55.614 10.243 35.299 75.929

Multiple Comparisons

Games-Howell

Dependent

Variable

(I) Group

Status

(J) Group

Status Std. Error Sig.

95% Confidence

Interval

217

Mean

Difference

(I-J)

Lower

Bound

Upper

Bound

Total Number of

Posts

Always PP PP -

Another

Role

1988.91* 614.405 .011 362.04 3615.78

PP -

Inactive 276.82 1320.608 .997 -3403.28 3956.91

Promoted

- PP 1789.21 754.678 .109 -287.70 3866.12

PP -

Another

Role

Always PP -1988.91* 614.405 .011 -3615.78 -362.04

PP -

Inactive -1712.10 1361.387 .598 -5473.31 2049.11

Promoted

- PP -199.70 823.958 .995 -2435.07 2035.66

PP -

Inactive

Always PP -276.82 1320.608 .997 -3956.91 3403.28

PP -

Another

Role

1712.10 1361.387 .598 -2049.11 5473.31

Promoted

- PP 1512.39 1430.182 .718 -2404.12 5428.90

Promoted

- PP

Always PP -1789.21 754.678 .109 -3866.12 287.70

PP -

Another

Role

199.70 823.958 .995 -2035.66 2435.07

PP -

Inactive -1512.39 1430.182 .718 -5428.90 2404.12

Word Count for

Posts

Always PP PP -

Another

Role

153099.14* 38158.744 .001 52299.52 253898.75

PP -

Inactive 52030.61 90701.104 .939

-

198129.34 302190.57

Promoted

- PP 153170.99* 47488.149 .012 26498.75 279843.24

PP -

Another

Role

Always PP -153099.14* 38158.744 .001

-

253898.75 -52299.52

PP -

Inactive -101068.52 85901.253 .648

-

341975.93 139838.88

Promoted

- PP 71.86 37516.121 1.000

-

103593.86 103737.57

218

PP -

Inactive

Always PP -52030.61 90701.104 .939

-

302190.57 198129.34

PP -

Another

Role

101068.52 85901.253 .648 -

139838.88 341975.93

Promoted

- PP 101140.38 90432.626 .682

-

148815.34 351096.10

Promoted

- PP

Always PP -153170.99* 47488.149 .012

-

279843.24 -26498.75

PP -

Another

Role

-71.86 37516.121 1.000 -

103737.57 103593.86

PP -

Inactive -101140.38 90432.626 .682

-

351096.10 148815.34

Reputation Always PP PP -

Another

Role

2672.60* 392.902 .000 1632.21 3712.99

PP -

Inactive 2937.75* 719.925 .002 982.65 4892.85

Promoted

- PP 3184.58* 386.332 .000 2159.16 4209.99

PP -

Another

Role

Always PP -2672.60* 392.902 .000 -3712.99 -1632.21

PP -

Inactive 265.15 641.553 .976 -1528.73 2059.02

Promoted

- PP 511.97 206.276 .078 -40.08 1064.03

PP -

Inactive

Always PP -2937.75* 719.925 .002 -4892.85 -982.65

PP -

Another

Role

-265.15 641.553 .976 -2059.02 1528.73

Promoted

- PP 246.83 637.550 .980 -1539.81 2033.47

Promoted

- PP

Always PP -3184.58* 386.332 .000 -4209.99 -2159.16

PP -

Another

Role

-511.97 206.276 .078 -1064.03 40.08

PP -

Inactive -246.83 637.550 .980 -2033.47 1539.81

Number fo

Thanks

Received

Always PP PP -

Another

Role

1144.45* 182.931 .000 658.11 1630.80

219

PP -

Inactive 999.66* 213.390 .000 435.72 1563.61

Promoted

- PP 1022.25* 192.511 .000 512.13 1532.36

PP -

Another

Role

Always PP -1144.45* 182.931 .000 -1630.80 -658.11

PP -

Inactive -144.79 132.996 .700 -510.75 221.17

Promoted

- PP -122.21 95.984 .587 -384.20 139.78

PP -

Inactive

Always PP -999.66* 213.390 .000 -1563.61 -435.72

PP -

Another

Role

144.79 132.996 .700 -221.17 510.75

Promoted

- PP 22.58 145.893 .999 -374.02 419.18

Promoted

- PP

Always PP -1022.25* 192.511 .000 -1532.36 -512.13

PP -

Another

Role

122.21 95.984 .587 -139.78 384.20

PP -

Inactive -22.58 145.893 .999 -419.18 374.02

Thread Word

Count

Always PP PP -

Another

Role

6655.26 2544.096 .052 -41.85 13352.37

PP -

Inactive -2095.08 5446.695 .980 -17115.07 12924.91

Promoted

- PP 6996.25 2675.025 .055 -108.45 14100.96

PP -

Another

Role

Always PP -6655.26 2544.096 .052 -13352.37 41.85

PP -

Inactive -8750.34 5269.761 .368 -23423.57 5922.88

Promoted

- PP 340.99 2293.394 .999 -5838.40 6520.38

PP -

Inactive

Always PP 2095.08 5446.695 .980 -12924.91 17115.07

PP -

Another

Role

8750.34 5269.761 .368 -5922.88 23423.57

Promoted

- PP 9091.33 5334.202 .345 -5716.50 23899.16

Always PP -6996.25 2675.025 .055 -14100.96 108.45

220

Promoted

- PP

PP -

Another

Role

-340.99 2293.394 .999 -6520.38 5838.40

PP -

Inactive -9091.33 5334.202 .345 -23899.16 5716.50

Thread Count Always PP PP -

Another

Role

35.89* 11.341 .013 5.93 65.85

PP -

Inactive 11.16 17.143 .915 -34.91 57.23

Promoted

- PP 33.85 13.600 .074 -2.33 70.04

PP -

Another

Role

Always PP -35.89* 11.341 .013 -65.85 -5.93

PP -

Inactive -24.74 14.786 .360 -65.64 16.17

Promoted

- PP -2.04 10.475 .997 -30.87 26.79

PP -

Inactive

Always PP -11.16 17.143 .915 -57.23 34.91

PP -

Another

Role

24.74 14.786 .360 -16.17 65.64

Promoted

- PP 22.69 16.583 .528 -22.38 67.77

Promoted

- PP

Always PP -33.85 13.600 .074 -70.04 2.33

PP -

Another

Role

2.04 10.475 .997 -26.79 30.87

PP -

Inactive -22.69 16.583 .528 -67.77 22.38

av_numthanks Always PP PP -

Another

Role

-6.1424 3.44473 .301 -15.5211 3.2363

PP -

Inactive -18.3890 10.11234 .297 -46.9544 10.1764

Promoted

- PP -7.2779 5.85019 .610 -24.1071 9.5514

PP -

Another

Role

Always PP 6.1424 3.44473 .301 -3.2363 15.5211

PP -

Inactive -12.2466 10.65706 .664 -41.8247 17.3315

Promoted

- PP -1.1355 6.74820 .998 -19.6769 17.4060

Always PP 18.3890 10.11234 .297 -10.1764 46.9544

221

PP -

Inactive

PP -

Another

Role

12.2466 10.65706 .664 -17.3315 41.8247

Promoted

- PP 11.1112 11.65896 .777 -20.7058 42.9281

Promoted

- PP

Always PP 7.2779 5.85019 .610 -9.5514 24.1071

PP -

Another

Role

1.1355 6.74820 .998 -17.4060 19.6769

PP -

Inactive -11.1112 11.65896 .777 -42.9281 20.7058

Average Thread

Word Count

Always PP PP -

Another

Role

-93.3303 163.99529 .941 -537.8052 351.1445

PP -

Inactive -25.3123 68.08292 .982 -207.9713 157.3468

Promoted

- PP -61.7917 136.92407 .969 -448.6404 325.0570

PP -

Another

Role

Always PP 93.3303 163.99529 .941 -351.1445 537.8052

PP -

Inactive 68.0181 167.74895 .977 -384.9106 520.9467

Promoted

- PP 31.5386 205.55444 .999 -518.0785 581.1557

PP -

Inactive

Always PP 25.3123 68.08292 .982 -157.3468 207.9713

PP -

Another

Role

-68.0181 167.74895 .977 -520.9467 384.9106

Promoted

- PP -36.4794 141.39822 .994 -432.0379 359.0791

Promoted

- PP

Always PP 61.7917 136.92407 .969 -325.0570 448.6404

PP -

Another

Role

-31.5386 205.55444 .999 -581.1557 518.0785

PP -

Inactive 36.4794 141.39822 .994 -359.0791 432.0379

Average Post

Word Count

Always PP PP -

Another

Role

7.1692 9.34271 .869 -17.4482 31.7865

PP -

Inactive 11.0603 9.19403 .628 -13.3561 35.4768

Promoted

- PP 7.0113 15.19682 .967 -35.1537 49.1763

222

PP -

Another

Role

Always PP -7.1692 9.34271 .869 -31.7865 17.4482

PP -

Inactive 3.8912 9.26684 .975 -20.8427 28.6250

Promoted

- PP -.1579 15.24098 1.000 -42.4428 42.1271

PP -

Inactive

Always PP -11.0603 9.19403 .628 -35.4768 13.3561

PP -

Another

Role

-3.8912 9.26684 .975 -28.6250 20.8427

Promoted

- PP -4.0490 15.15029 .993 -46.1979 38.0999

Promoted

- PP

Always PP -7.0113 15.19682 .967 -49.1763 35.1537

PP -

Another

Role

.1579 15.24098 1.000 -42.1271 42.4428

PP -

Inactive 4.0490 15.15029 .993 -38.0999 46.1979

Based on observed means.

The error term is Mean Square(Error) = 1678.797.

*. The mean difference is significant at the

Between-Subjects Factors

Value Label N

Group Status 1 Always PP 43

2 PP - Another Role 29

3 PP - Inactive 19

4 Promoted - PP 16

Descriptive Statistics

Group Status Mean Std. Deviation N

Total Number of Posts Always PP 4077.40 2400.971 43

PP - Another Role 2088.48 2656.973 29

PP - Inactive 3800.58 5530.725 19

Promoted - PP 2288.19 2639.625 16

Total 3221.64 3342.033 107

Word Count for Posts Always PP 249215.93 222554.996 43

PP - Another Role 96116.79 93926.351 29

223

PP - Inactive 197185.32 366635.303 19

Promoted - PP 96044.94 132860.645 16

Total 175578.62 228572.949 107

Reputation Always PP 3705.33 2369.196 43

PP - Another Role 1032.72 831.390 29

PP - Inactive 767.58 2714.287 19

Promoted - PP 520.75 547.214 16

Total 1983.12 2395.415 107

Number fo Thanks Received Always PP 1468.56 1148.123 43

PP - Another Role 324.10 285.366 29

PP - Inactive 468.89 531.710 19

Promoted - PP 446.31 320.123 16

Total 828.01 941.988 107

Thread Word Count Always PP 12369.81 13413.554 43

PP - Another Role 5714.55 8145.960 29

PP - Inactive 14464.89 22003.684 19

Promoted - PP 5373.56 6895.209 16

Total 9891.91 13851.267 107

Thread Count Always PP 60.79 66.206 43

PP - Another Role 24.90 27.817 29

PP - Inactive 49.63 60.390 19

Promoted - PP 26.94 36.450 16

Total 44.02 54.884 107

av_numthanks Always PP 3.9824 3.44720 43

PP - Another Role 10.1248 18.33318 29

PP - Inactive 22.3714 44.01906 19

Promoted - PP 11.2603 23.30610 16

Total 10.0008 23.27579 107

Average Thread Word Count Always PP 252.1730 269.97332 43

PP - Another Role 345.5033 854.85891 29

PP - Inactive 277.4852 236.35806 19

Promoted - PP 313.9647 522.35140 16

Total 291.2027 521.10508 107

Average Post Word Count Always PP 62.6250 42.98549 43

PP - Another Role 55.4558 35.84885 29

PP - Inactive 51.5646 28.10025 19

Promoted - PP 55.6137 54.84119 16

Total 57.6695 40.61902 107

224

Box's Test of Equality of

Covariance Matricesa

Box's M 843.036

F 5.042

df1 135

df2 10475.674

Sig. .000

Tests the null hypothesis

that the observed

covariance matrices of the

dependent variables are

equal across groups.

a. Design: Intercept + Status

Descriptive Statistics

Group Status Mean Std. Deviation N

Total Number of Posts Always PP 4077.40 2400.971 43

PP - Another Role 2088.48 2656.973 29

PP - Inactive 3800.58 5530.725 19

Promoted - PP 2288.19 2639.625 16

Total 3221.64 3342.033 107

Word Count for Posts Always PP 249215.93 222554.996 43

PP - Another Role 96116.79 93926.351 29

PP - Inactive 197185.32 366635.303 19

Promoted - PP 96044.94 132860.645 16

Total 175578.62 228572.949 107

Reputation Always PP 3705.33 2369.196 43

PP - Another Role 1032.72 831.390 29

PP - Inactive 767.58 2714.287 19

Promoted - PP 520.75 547.214 16

Total 1983.12 2395.415 107

Number fo Thanks Received Always PP 1468.56 1148.123 43

PP - Another Role 324.10 285.366 29

PP - Inactive 468.89 531.710 19

Promoted - PP 446.31 320.123 16

Total 828.01 941.988 107

Thread Word Count Always PP 12369.81 13413.554 43

225

PP - Another Role 5714.55 8145.960 29

PP - Inactive 14464.89 22003.684 19

Promoted - PP 5373.56 6895.209 16

Total 9891.91 13851.267 107

Thread Count Always PP 60.79 66.206 43

PP - Another Role 24.90 27.817 29

PP - Inactive 49.63 60.390 19

Promoted - PP 26.94 36.450 16

Total 44.02 54.884 107

av_numthanks Always PP 3.9824 3.44720 43

PP - Another Role 10.1248 18.33318 29

PP - Inactive 22.3714 44.01906 19

Promoted - PP 11.2603 23.30610 16

Total 10.0008 23.27579 107

Average Thread Word Count Always PP 252.1730 269.97332 43

PP - Another Role 345.5033 854.85891 29

PP - Inactive 277.4852 236.35806 19

Promoted - PP 313.9647 522.35140 16

Total 291.2027 521.10508 107

Average Post Word Count Always PP 62.6250 42.98549 43

PP - Another Role 55.4558 35.84885 29

PP - Inactive 51.5646 28.10025 19

Promoted - PP 55.6137 54.84119 16

Total 57.6695 40.61902 107

Multivariate Testsa

Effect Value F

Hypothesis

df Error df Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerd

Intercept Pillai's

Trace .830 51.621b 9.000 95.000 .000 .830 464.586 1.000

Wilks'

Lambda .170 51.621b 9.000 95.000 .000 .830 464.586 1.000

Hotelling's

Trace 4.890 51.621b 9.000 95.000 .000 .830 464.586 1.000

Roy's

Largest

Root

4.890 51.621b 9.000 95.000 .000 .830 464.586 1.000

226

Status Pillai's

Trace .625 2.835 27.000 291.000 .000 .208 76.534 1.000

Wilks'

Lambda .438 3.366 27.000 278.091 .000 .241 88.138 1.000

Hotelling's

Trace 1.143 3.965 27.000 281.000 .000 .276 107.048 1.000

Roy's

Largest

Root

1.006 10.841c 9.000 97.000 .000 .501 97.572 1.000

a. Design: Intercept + Status

b. Exact statistic

c. The statistic is an upper bound on F that yields a lower bound on the significance level.

d. Computed using alpha =

Descriptive Statistics

Group Status Mean Std. Deviation N

Total Number of Posts Always PP 4077.40 2400.971 43

PP - Another Role 2088.48 2656.973 29

PP - Inactive 3800.58 5530.725 19

Promoted - PP 2288.19 2639.625 16

Total 3221.64 3342.033 107

Word Count for Posts Always PP 249215.93 222554.996 43

PP - Another Role 96116.79 93926.351 29

PP - Inactive 197185.32 366635.303 19

Promoted - PP 96044.94 132860.645 16

Total 175578.62 228572.949 107

Reputation Always PP 3705.33 2369.196 43

PP - Another Role 1032.72 831.390 29

PP - Inactive 767.58 2714.287 19

Promoted - PP 520.75 547.214 16

Total 1983.12 2395.415 107

Number fo Thanks Received Always PP 1468.56 1148.123 43

PP - Another Role 324.10 285.366 29

PP - Inactive 468.89 531.710 19

Promoted - PP 446.31 320.123 16

Total 828.01 941.988 107

Thread Word Count Always PP 12369.81 13413.554 43

PP - Another Role 5714.55 8145.960 29

PP - Inactive 14464.89 22003.684 19

Promoted - PP 5373.56 6895.209 16

227

Total 9891.91 13851.267 107

Thread Count Always PP 60.79 66.206 43

PP - Another Role 24.90 27.817 29

PP - Inactive 49.63 60.390 19

Promoted - PP 26.94 36.450 16

Total 44.02 54.884 107

av_numthanks Always PP 3.9824 3.44720 43

PP - Another Role 10.1248 18.33318 29

PP - Inactive 22.3714 44.01906 19

Promoted - PP 11.2603 23.30610 16

Total 10.0008 23.27579 107

Average Thread Word Count Always PP 252.1730 269.97332 43

PP - Another Role 345.5033 854.85891 29

PP - Inactive 277.4852 236.35806 19

Promoted - PP 313.9647 522.35140 16

Total 291.2027 521.10508 107

Average Post Word Count Always PP 62.6250 42.98549 43

PP - Another Role 55.4558 35.84885 29

PP - Inactive 51.5646 28.10025 19

Promoted - PP 55.6137 54.84119 16

Total 57.6695 40.61902 107

Levene's Test of Equality of Error Variancesa

F df1 df2 Sig.

Total Number of Posts 5.710 3 103 .001

Word Count for Posts 3.559 3 103 .017

Reputation 7.041 3 103 .000

Number fo Thanks Received 8.729 3 103 .000

Thread Word Count 6.454 3 103 .000

Thread Count 4.003 3 103 .010

av_numthanks 9.263 3 103 .000

Average Thread Word

Count 1.068 3 103 .366

Average Post Word Count 1.851 3 103 .143

Tests the null hypothesis that the error variance of the dependent variable is equal across groups.

a. Design: Intercept + Status

Tests of Between-Subjects Effects

228

Source

Dependent

Variable

Type III Sum of

Squares df Mean Square F

Si

g.

Partial

Eta

Squar

ed

Noncen

t.

Parame

ter

Observ

ed

Powerj

Correct

ed

Model

Total

Number of

Posts

89036527.915a 3 29678842.638 2.792 .04

4 .075 8.376 .658

Word

Count for

Posts

526356617972.

696b 3

175452205990.

899 3.606

.01

6 .095 10.818 .780

Reputation 216021652.554

c 3 72007217.518

18.91

0

.00

0 .355 56.731 1.000

Number fo

Thanks

Received

29788114.469d 3 9929371.490 15.91

3

.00

0 .317 47.739 1.000

Thread

Word

Count

1494058711.65

4e 3 498019570.551 2.722

.04

8 .073 8.167 .646

Thread

Count 27966.798f 3 9322.266 3.296

.02

3 .088 9.888 .738

av_numtha

nks 4490.945g 3 1496.982 2.913

.03

8 .078 8.738 .679

Average

Thread

Word

Count

162875.708h 3 54291.903 .195 .89

9 .006 .586 .085

Average

Post Word

Count

1973.796i 3 657.932 .392 .75

9 .011 1.176 .125

Interce

pt

Total

Number of

Posts

868723211.285 1 868723211.285 81.72

3

.00

0 .442 81.723 1.000

Word

Count for

Posts

235878011055

0.029 1

2358780110550

.029

48.47

8

.00

0 .320 48.478 1.000

Reputation 210083924.349 1 210083924.349

55.17

1

.00

0 .349 55.171 1.000

Number fo

Thanks

Received

42416539.275 1 42416539.275 67.97

7

.00

0 .398 67.977 1.000

229

Thread

Word

Count

8319194986.20

3 1

8319194986.20

3

45.47

5

.00

0 .306 45.475 1.000

Thread

Count 152294.169 1 152294.169

53.84

3

.00

0 .343 53.843 1.000

av_numtha

nks 13183.317 1 13183.317

25.65

1

.00

0 .199 25.651 .999

Average

Thread

Word

Count

8179671.911 1 8179671.911 29.43

6

.00

0 .222 29.436 1.000

Average

Post Word

Count

293524.734 1 293524.734 174.8

42

.00

0 .629 174.842 1.000

Status Total

Number of

Posts

89036527.915 3 29678842.638 2.792 .04

4 .075 8.376 .658

Word

Count for

Posts

526356617972.

697 3

175452205990.

899 3.606

.01

6 .095 10.818 .780

Reputation 216021652.554 3 72007217.518

18.91

0

.00

0 .355 56.731 1.000

Number fo

Thanks

Received

29788114.469 3 9929371.490 15.91

3

.00

0 .317 47.739 1.000

Thread

Word

Count

1494058711.65

4 3 498019570.551 2.722

.04

8 .073 8.167 .646

Thread

Count 27966.798 3 9322.266 3.296

.02

3 .088 9.888 .738

av_numtha

nks 4490.945 3 1496.982 2.913

.03

8 .078 8.738 .679

Average

Thread

Word

Count

162875.708 3 54291.903 .195 .89

9 .006 .586 .085

Average

Post Word

Count

1973.796 3 657.932 .392 .75

9 .011 1.176 .125

Error Total

Number of

Posts

1094896946.59

0

10

3 10630067.443

230

Word

Count for

Posts

501167624641

8.593

10

3

48657050936.1

03

Reputation 392207672.867

10

3 3807841.484

Number fo

Thanks

Received

64270052.521 10

3 623981.092

Thread

Word

Count

18842847071.4

11

10

3 182940262.829

Thread

Count 291333.164

10

3 2828.477

av_numtha

nks 52935.860

10

3 513.940

Average

Thread

Word

Count

28621477.403 10

3 277878.421

Average

Post Word

Count

172916.110 10

3 1678.797

Total Total

Number of

Posts

2294486004.00

0

10

7

Word

Count for

Posts

883661288771

6.000

10

7

Reputation 1029035808.00

0

10

7

Number fo

Thanks

Received

167417311.000 10

7

Thread

Word

Count

30806835992.0

00

10

7

Thread

Count 526628.000

10

7

av_numtha

nks 68128.484

10

7

231

Average

Thread

Word

Count

37857847.209 10

7

Average

Post Word

Count

530747.802 10

7

Correct

ed

Total

Total

Number of

Posts

1183933474.50

5

10

6

Word

Count for

Posts

553803286439

1.289

10

6

Reputation 608229325.421

10

6

Number fo

Thanks

Received

94058166.991 10

6

Thread

Word

Count

20336905783.0

65

10

6

Thread

Count 319299.963

10

6

av_numtha

nks 57426.805

10

6

Average

Thread

Word

Count

28784353.111 10

6

Average

Post Word

Count

174889.906 10

6

a. R Squared = .075 (Adjusted R Squared = .048)

b. R Squared = .095 (Adjusted R Squared = .069)

c. R Squared = .355 (Adjusted R Squared = .336)

d. R Squared = .317 (Adjusted R Squared = .297)

e. R Squared = .073 (Adjusted R Squared = .046)

f. R Squared = .088 (Adjusted R Squared = .061)

g. R Squared = .078 (Adjusted R Squared = .051)

h. R Squared = .006 (Adjusted R Squared = -.023)

i. R Squared = .011 (Adjusted R Squared = -.018)

j. Computed using alpha =

232

Parameter Estimates

Dependent

Variable

Parame

ter B

Std.

Error t

Si

g.

95% Confidence

Interval

Partial

Eta

Squar

ed

Noncen

t.

Parame

ter

Observ

ed

Powerb

Lower

Bound

Upper

Bound

Total

Number of

Posts

Interce

pt

2288.18

7 815.095

2.8

07

.00

6 671.640

3904.73

5 .071 2.807 .794

[Status

=1]

1789.20

8 954.772

1.8

74

.06

4 -104.358

3682.77

3 .033 1.874 .459

[Status

=2] -199.705 1015.34

9

-

.19

7

.84

4

-

2213.41

0

1814.00

0 .000 .197 .054

[Status

=3]

1512.39

1

1106.28

0

1.3

67

.17

5 -681.655

3706.43

8 .018 1.367 .273

[Status

=4] 0a . . . . . . . .

Word

Count for

Posts

Interce

pt 96044.9

38

55145.8

58

1.7

42

.08

5

-

13323.8

66

205413.

741 .029 1.742 .407

[Status

=1]

153170.

993

64595.8

59

2.3

71

.02

0

25060.3

42

281281.

644 .052 2.371 .651

[Status

=2] 71.856 68694.2

14

.00

1

.99

9

-

136166.

916

136310.

627 .000 .001 .050

[Status

=3] 101140.

378

74846.2

63

1.3

51

.18

0

-

47299.5

32

249580.

288 .017 1.351 .268

[Status

=4] 0a . . . . . . . .

Reputation Interce

pt 520.750 487.842

1.0

67

.28

8 -446.770

1488.27

0 .011 1.067 .185

[Status

=1]

3184.57

6 571.441

5.5

73

.00

0

2051.25

8

4317.89

4 .232 5.573 1.000

[Status

=2] 511.974 607.696

.84

2

.40

1 -693.248

1717.19

7 .007 .842 .133

[Status

=3] 246.829 662.120 .37

3

.71

0

-

1066.33

0

1559.98

8 .001 .373 .066

233

[Status

=4] 0a . . . . . . . .

Number fo

Thanks

Received

Interce

pt 446.312 197.481

2.2

60

.02

6 54.655 837.970 .047 2.260 .610

[Status

=1]

1022.24

6 231.322

4.4

19

.00

0 563.472

1481.01

9 .159 4.419 .992

[Status

=2] -122.209 245.999

-

.49

7

.62

0 -610.090 365.672 .002 .497 .078

[Status

=3] 22.582 268.030

.08

4

.93

3 -508.991 554.156 .000 .084 .051

[Status

=4] 0a . . . . . . . .

Thread

Word

Count

Interce

pt 5373.56

2

3381.38

5

1.5

89

.11

5

-

1332.61

8

12079.7

43 .024 1.589 .350

[Status

=1]

6996.25

1

3960.83

2

1.7

66

.08

0 -859.125

14851.6

28 .029 1.766 .417

[Status

=2] 340.989 4212.13

1

.08

1

.93

6

-

8012.78

0

8694.75

9 .000 .081 .051

[Status

=3]

9091.33

2

4589.35

7

1.9

81

.05

0 -10.576

18193.2

40 .037 1.981 .501

[Status

=4] 0a . . . . . . . .

Thread

Count

Interce

pt 26.937 13.296

2.0

26

.04

5 .568 53.307 .038 2.026 .519

[Status

=1] 33.853 15.574

2.1

74

.03

2 2.965 64.741 .044 2.174 .577

[Status

=2] -2.041 16.562

-

.12

3

.90

2 -34.889 30.807 .000 .123 .052

[Status

=3] 22.694 18.046

1.2

58

.21

1 -13.095 58.483 .015 1.258 .238

[Status

=4] 0a . . . . . . . .

av_numtha

nks

Interce

pt 11.260 5.668

1.9

87

.05

0 .020 22.501 .037 1.987 .503

[Status

=1] -7.278 6.639

-

1.0

96

.27

6 -20.444 5.889 .012 1.096 .192

234

[Status

=2] -1.135 7.060

-

.16

1

.87

3 -15.137 12.866 .000 .161 .053

[Status

=3] 11.111 7.692

1.4

44

.15

2 -4.145 26.367 .020 1.444 .299

[Status

=4] 0a . . . . . . . .

Average

Thread

Word

Count

Interce

pt 313.965 131.785

2.3

82

.01

9 52.599 575.330 .052 2.382 .655

[Status

=1] -61.792 154.369

-

.40

0

.69

0 -367.946 244.362 .002 .400 .068

[Status

=2] 31.539 164.163

.19

2

.84

8 -294.040 357.117 .000 .192 .054

[Status

=3] -36.479 178.865

-

.20

4

.83

9 -391.215 318.257 .000 .204 .055

[Status

=4] 0a . . . . . . . .

Average

Post Word

Count

Interce

pt 55.614 10.243

5.4

29

.00

0 35.299 75.929 .223 5.429 1.000

[Status

=1] 7.011 11.999

.58

4

.56

0 -16.785 30.808 .003 .584 .089

[Status

=2] -.158 12.760

-

.01

2

.99

0 -25.464 25.148 .000 .012 .050

[Status

=3] -4.049 13.903

-

.29

1

.77

1 -31.622 23.524 .001 .291 .060

[Status

=4] 0a . . . . . . . .

a. This parameter is set to zero because it is redundant.

b. Computed using alpha =

Group Status

Dependent Variable Group Status Mean Std. Error

95% Confidence Interval

Lower

Bound

Upper

Bound

Total Number of Posts Always PP 4077.395 497.203 3091.310 5063.480

235

PP - Another

Role 2088.483 605.437 887.741 3289.224

PP - Inactive 3800.579 747.982 2317.133 5284.025

Promoted - PP 2288.187 815.095 671.640 3904.735

Word Count for Posts Always PP 249215.930 33638.658 182501.586 315930.275

PP - Another

Role 96116.793 40961.315 14879.685 177353.901

PP - Inactive 197185.316 50605.310 96821.619 297549.013

Promoted - PP 96044.938 55145.858 -13323.866 205413.741

Reputation Always PP 3705.326 297.581 3115.144 4295.507

PP - Another

Role 1032.724 362.360 314.068 1751.380

PP - Inactive 767.579 447.675 -120.278 1655.436

Promoted - PP 520.750 487.842 -446.770 1488.270

Number fo Thanks

Received

Always PP 1468.558 120.462 1229.649 1707.467

PP - Another

Role 324.103 146.685 33.188 615.019

PP - Inactive 468.895 181.221 109.485 828.304

Promoted - PP 446.312 197.481 54.655 837.970

Thread Word Count Always PP 12369.814 2062.626 8279.082 16460.546

PP - Another

Role 5714.552 2511.630 733.326 10695.777

PP - Inactive 14464.895 3102.972 8310.882 20618.908

Promoted - PP 5373.562 3381.385 -1332.618 12079.743

Thread Count Always PP 60.791 8.110 44.706 76.876

PP - Another

Role 24.897 9.876 5.310 44.483

PP - Inactive 49.632 12.201 25.434 73.830

Promoted - PP 26.937 13.296 .568 53.307

av_numthanks Always PP 3.982 3.457 -2.874 10.839

PP - Another

Role 10.125 4.210 1.776 18.474

PP - Inactive 22.371 5.201 12.057 32.686

Promoted - PP 11.260 5.668 .020 22.501

Average Thread Word

Count

Always PP 252.173 80.388 92.742 411.604

PP - Another

Role 345.503 97.888 151.366 539.641

PP - Inactive 277.485 120.935 37.640 517.331

Promoted - PP 313.965 131.785 52.599 575.330

Average Post Word Count Always PP 62.625 6.248 50.233 75.017

236

PP - Another

Role 55.456 7.609 40.366 70.546

PP - Inactive 51.565 9.400 32.922 70.207

Promoted - PP 55.614 10.243 35.299 75.929

Multiple Comparisons

Games-Howell

Dependent

Variable

(I) Group

Status

(J) Group

Status

Mean

Difference

(I-J) Std. Error Sig.

95% Confidence

Interval

Lower

Bound

Upper

Bound

Total Number of

Posts

Always PP PP -

Another

Role

1988.91* 614.405 .011 362.04 3615.78

PP -

Inactive 276.82 1320.608 .997 -3403.28 3956.91

Promoted

- PP 1789.21 754.678 .109 -287.70 3866.12

PP -

Another

Role

Always PP -1988.91* 614.405 .011 -3615.78 -362.04

PP -

Inactive -1712.10 1361.387 .598 -5473.31 2049.11

Promoted

- PP -199.70 823.958 .995 -2435.07 2035.66

PP -

Inactive

Always PP -276.82 1320.608 .997 -3956.91 3403.28

PP -

Another

Role

1712.10 1361.387 .598 -2049.11 5473.31

Promoted

- PP 1512.39 1430.182 .718 -2404.12 5428.90

Promoted

- PP

Always PP -1789.21 754.678 .109 -3866.12 287.70

PP -

Another

Role

199.70 823.958 .995 -2035.66 2435.07

PP -

Inactive -1512.39 1430.182 .718 -5428.90 2404.12

237

Word Count for

Posts

Always PP PP -

Another

Role

153099.14* 38158.744 .001 52299.52 253898.75

PP -

Inactive 52030.61 90701.104 .939

-

198129.34 302190.57

Promoted

- PP 153170.99* 47488.149 .012 26498.75 279843.24

PP -

Another

Role

Always PP -153099.14* 38158.744 .001

-

253898.75 -52299.52

PP -

Inactive -101068.52 85901.253 .648

-

341975.93 139838.88

Promoted

- PP 71.86 37516.121 1.000

-

103593.86 103737.57

PP -

Inactive

Always PP -52030.61 90701.104 .939

-

302190.57 198129.34

PP -

Another

Role

101068.52 85901.253 .648 -

139838.88 341975.93

Promoted

- PP 101140.38 90432.626 .682

-

148815.34 351096.10

Promoted

- PP

Always PP -153170.99* 47488.149 .012

-

279843.24 -26498.75

PP -

Another

Role

-71.86 37516.121 1.000 -

103737.57 103593.86

PP -

Inactive -101140.38 90432.626 .682

-

351096.10 148815.34

Reputation Always PP PP -

Another

Role

2672.60* 392.902 .000 1632.21 3712.99

PP -

Inactive 2937.75* 719.925 .002 982.65 4892.85

Promoted

- PP 3184.58* 386.332 .000 2159.16 4209.99

PP -

Another

Role

Always PP -2672.60* 392.902 .000 -3712.99 -1632.21

PP -

Inactive 265.15 641.553 .976 -1528.73 2059.02

Promoted

- PP 511.97 206.276 .078 -40.08 1064.03

Always PP -2937.75* 719.925 .002 -4892.85 -982.65

238

PP -

Inactive

PP -

Another

Role

-265.15 641.553 .976 -2059.02 1528.73

Promoted

- PP 246.83 637.550 .980 -1539.81 2033.47

Promoted

- PP

Always PP -3184.58* 386.332 .000 -4209.99 -2159.16

PP -

Another

Role

-511.97 206.276 .078 -1064.03 40.08

PP -

Inactive -246.83 637.550 .980 -2033.47 1539.81

Number fo

Thanks

Received

Always PP PP -

Another

Role

1144.45* 182.931 .000 658.11 1630.80

PP -

Inactive 999.66* 213.390 .000 435.72 1563.61

Promoted

- PP 1022.25* 192.511 .000 512.13 1532.36

PP -

Another

Role

Always PP -1144.45* 182.931 .000 -1630.80 -658.11

PP -

Inactive -144.79 132.996 .700 -510.75 221.17

Promoted

- PP -122.21 95.984 .587 -384.20 139.78

PP -

Inactive

Always PP -999.66* 213.390 .000 -1563.61 -435.72

PP -

Another

Role

144.79 132.996 .700 -221.17 510.75

Promoted

- PP 22.58 145.893 .999 -374.02 419.18

Promoted

- PP

Always PP -1022.25* 192.511 .000 -1532.36 -512.13

PP -

Another

Role

122.21 95.984 .587 -139.78 384.20

PP -

Inactive -22.58 145.893 .999 -419.18 374.02

Thread Word

Count

Always PP PP -

Another

Role

6655.26 2544.096 .052 -41.85 13352.37

PP -

Inactive -2095.08 5446.695 .980 -17115.07 12924.91

Promoted

- PP 6996.25 2675.025 .055 -108.45 14100.96

239

PP -

Another

Role

Always PP -6655.26 2544.096 .052 -13352.37 41.85

PP -

Inactive -8750.34 5269.761 .368 -23423.57 5922.88

Promoted

- PP 340.99 2293.394 .999 -5838.40 6520.38

PP -

Inactive

Always PP 2095.08 5446.695 .980 -12924.91 17115.07

PP -

Another

Role

8750.34 5269.761 .368 -5922.88 23423.57

Promoted

- PP 9091.33 5334.202 .345 -5716.50 23899.16

Promoted

- PP

Always PP -6996.25 2675.025 .055 -14100.96 108.45

PP -

Another

Role

-340.99 2293.394 .999 -6520.38 5838.40

PP -

Inactive -9091.33 5334.202 .345 -23899.16 5716.50

Thread Count Always PP PP -

Another

Role

35.89* 11.341 .013 5.93 65.85

PP -

Inactive 11.16 17.143 .915 -34.91 57.23

Promoted

- PP 33.85 13.600 .074 -2.33 70.04

PP -

Another

Role

Always PP -35.89* 11.341 .013 -65.85 -5.93

PP -

Inactive -24.74 14.786 .360 -65.64 16.17

Promoted

- PP -2.04 10.475 .997 -30.87 26.79

PP -

Inactive

Always PP -11.16 17.143 .915 -57.23 34.91

PP -

Another

Role

24.74 14.786 .360 -16.17 65.64

Promoted

- PP 22.69 16.583 .528 -22.38 67.77

Promoted

- PP

Always PP -33.85 13.600 .074 -70.04 2.33

PP -

Another

Role

2.04 10.475 .997 -26.79 30.87

PP -

Inactive -22.69 16.583 .528 -67.77 22.38

240

av_numthanks Always PP PP -

Another

Role

-6.1424 3.44473 .301 -15.5211 3.2363

PP -

Inactive -18.3890 10.11234 .297 -46.9544 10.1764

Promoted

- PP -7.2779 5.85019 .610 -24.1071 9.5514

PP -

Another

Role

Always PP 6.1424 3.44473 .301 -3.2363 15.5211

PP -

Inactive -12.2466 10.65706 .664 -41.8247 17.3315

Promoted

- PP -1.1355 6.74820 .998 -19.6769 17.4060

PP -

Inactive

Always PP 18.3890 10.11234 .297 -10.1764 46.9544

PP -

Another

Role

12.2466 10.65706 .664 -17.3315 41.8247

Promoted

- PP 11.1112 11.65896 .777 -20.7058 42.9281

Promoted

- PP

Always PP 7.2779 5.85019 .610 -9.5514 24.1071

PP -

Another

Role

1.1355 6.74820 .998 -17.4060 19.6769

PP -

Inactive -11.1112 11.65896 .777 -42.9281 20.7058

Average Thread

Word Count

Always PP PP -

Another

Role

-93.3303 163.99529 .941 -537.8052 351.1445

PP -

Inactive -25.3123 68.08292 .982 -207.9713 157.3468

Promoted

- PP -61.7917 136.92407 .969 -448.6404 325.0570

PP -

Another

Role

Always PP 93.3303 163.99529 .941 -351.1445 537.8052

PP -

Inactive 68.0181 167.74895 .977 -384.9106 520.9467

Promoted

- PP 31.5386 205.55444 .999 -518.0785 581.1557

PP -

Inactive

Always PP 25.3123 68.08292 .982 -157.3468 207.9713

PP -

Another

Role

-68.0181 167.74895 .977 -520.9467 384.9106

Promoted

- PP -36.4794 141.39822 .994 -432.0379 359.0791

241

Promoted

- PP

Always PP 61.7917 136.92407 .969 -325.0570 448.6404

PP -

Another

Role

-31.5386 205.55444 .999 -581.1557 518.0785

PP -

Inactive 36.4794 141.39822 .994 -359.0791 432.0379

Average Post

Word Count

Always PP PP -

Another

Role

7.1692 9.34271 .869 -17.4482 31.7865

PP -

Inactive 11.0603 9.19403 .628 -13.3561 35.4768

Promoted

- PP 7.0113 15.19682 .967 -35.1537 49.1763

PP -

Another

Role

Always PP -7.1692 9.34271 .869 -31.7865 17.4482

PP -

Inactive 3.8912 9.26684 .975 -20.8427 28.6250

Promoted

- PP -.1579 15.24098 1.000 -42.4428 42.1271

PP -

Inactive

Always PP -11.0603 9.19403 .628 -35.4768 13.3561

PP -

Another

Role

-3.8912 9.26684 .975 -28.6250 20.8427

Promoted

- PP -4.0490 15.15029 .993 -46.1979 38.0999

Promoted

- PP

Always PP -7.0113 15.19682 .967 -49.1763 35.1537

PP -

Another

Role

.1579 15.24098 1.000 -42.1271 42.4428

PP -

Inactive 4.0490 15.15029 .993 -38.0999 46.1979

Based on observed means.

The error term is Mean Square(Error) = 1678.797.

*. The mean difference is significant at the

242

Appendix 4: Tables from Quantitative Chapter

4.3.1. Individual Synopses for Eight LWP Community Members

4.3.1.1. Guy Fawkes

Figure 4.1. Chart displaying posting behaviour from 2006 – 2011 for LWP member

Guy Fawkes

Figure 4.2. Chart displaying thread starting behaviour from 2006 – 2011 for LWP

member Guy Fawkes

0

200

400

600

800

1000

2006 2007 2008 2009 2010 2011

To

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r Y

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Total Number of Posts (Per Year)

0

5

10

15

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2006 2007 2008 2009 2010 2011

To

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Total Number of Threads Started (Per Year)

243

Figure 4.3. Chart displaying average word count behaviour from 2006 – 2011 for LWP

member Guy Fawkes

Synopsis of Guy Fawkes

Guy Fawkes was an influential member within the community between December 2010

– December 2011. Based on the thread frequency, the highest number of threads started

within a year appears to correspond with his influential status at the time; however, his

posting peak was in 2007; though 2011 also had a high volume of threads posting in

relation to previous years in the community. With regards to word count, his average

word count appears to start quite high, then gradually peak when he was most

influential. As such, the notion of high word count, high-thread starting and a relatively

high posting behaviour appear to correspond with this user in particular.

4.3.1.2. Black Magician

0

50

100

150

200

2006 2007 2008 2009 2010 2011

Year

Average Word Count (Per Year)

Average Thread Word Count Average Post Word Count

244

Figure 4.4. Chart displaying posting behaviour from 2005 – 2011 for LWP Black

Magician.

Figure 4.5. Chart displaying thread starting behaviour from 2005 – 2011 for LWP

Black Magician.

0

100

200

300

400

500

600

2005 2006 2007 2008 2009 2010 2011

To

tal

Nu

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r o

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Total Number of Posts (Per Year)

0

2

4

6

8

10

2005 2006 2007 2008 2009 2010 2011

To

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Year

Number of Threads Started (Per Year)

245

Figure 4.6. Chart displaying average word count behaviour from 2005 – 2011 for LWP

Black Magician.

Synopsis of Black Magician

Black Magician’s posting frequency appears to correspond to some degree with his

social status. This user was deemed to be influential throughout the cluster analysis

(from 2009-2011). While his posting behaviour increases with years in the community,

his thread starting behaviour does not display a huge variation for each year. Thread

behaviour is not consistent with postings as the highest number of threads started per

year was in 2006 (which is not included in the cluster analysis to assess influence at

that time). However, 2010 does show a high number of thread-starting behaviour,

which is when this user was supposedly influential within the community, showing that

thread starting behaviour may well be a factor entwined in status.

4.3.1.3. Community Member

0

200

400

600

800

1000

2005 2006 2008 2009 2010 2011

Year

Average Word Count (Per Year)

Average Post Word Average Thread Word

246

Figure 4.7. Chart displaying posting behaviour from 2008 – 2011 for LWP member

Community Member.

Figure 4.8. Chart displaying total number of thread started each from 2008 – 2011 for

LWP member Community Member.

0

50

100

150

200

250

300

350

2009 2010 2011To

tal

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Total Number of Posts (Per Year)

0

1

2

3

4

2009 2010 2011

To

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rea

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Sta

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Years

Total Number of Threads Started (Per Year)

0

1000

2000

3000

2009 2010 2011

Year

Average Word Count (Per Year)

Average Thread Word Count Average Post Word Count

247

Figure 4.9. Chart displaying average word count for threads and posts from 2008 –

2011 for LWP member Community Member.

Synopsis of Community Member

Community Member’s online behaviour appears to correspond with status within the

community. High volumes of posts and threads-started parallel his high status within

the community, thus confirming the transition of social roles in the table above as there

was limited activity when this user was a contributor within LWP. Average word count

behaviour also appears consistent with the notion of status.

4.3.1.4. Guerrilla Warfare

Figure 4.10. Chart Displaying posting behaviour from 2004 – 2010 for LWP member

Guerrilla Warfare.

0

50

100

150

200

250

2004 2005 2006 2007 2008 2009 2010To

tal

Nu

mb

er

of

Po

sts

Years

Total Number of Posts (Per Year)

248

Figure 4.11. Chart displaying total number of thread started each from 2004 – 2010 for

LWP member Guerrilla Warfare.

Figure 4.12. Chart displaying average word count for threads and posts from 2004 –

2010 for LWP member Guerrilla Warfare.

Synopsis of Guerrilla Warfare

Guerrilla Warfare’s behavioural pattern also confirms some aspects of social capital

and high number of posts and threads-started. This user was influential in 2009 then

was demoted to a lesser social role. Whilst the highest number of posts and threads is

in 2004, the cluster analysis presented in the table above only examined behaviour

between 2009 – 2011. However, from the information presented above, it suggests that

0

1

2

3

4

5

6

7

2004 2005 2006 2007 2008 2009 2010To

tal

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Sta

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Year

Total Number of Threads Started (Per Year)

0

200

400

600

800

1000

2004 2005 2006 2007 2008 2009 2010

Year

Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

249

this user had been influential in the community for some time. There is no available

information for this community member in 2011 as he was banned from the LWP

community.

4.4.2 Individual LWP Members that Lost Status

4.4.2.1. Skinhead

Figure 4.13. Chart displaying posting behaviour from 2004 – 2010 for LWP member

Skinhead.

Figure 4.14. Chart displaying total number of thread started each from 2004 – 2010 for

LWP member Skinhead.

0

50

100

150

200

250

2004 2005 2006 2007 2008 2009 2010

To

tal

Nu

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er

of

Po

sts

Year

Total Number of Post (Per Year)

0

1

2

3

2004 2005 2006 2007 2008 2009 2010

To

tal

Nu

mb

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of

Th

rea

ds

Sta

rte

d

Year)

Total Number of Threads Started (Per Year)

250

Figure 4.15. Chart displaying average word count for threads and posts from 2004 –

2010 for LWP member Skinhead.

Synopsis of Skinhead

The above role transition table states that this member was influential in December

2010. With regards to thread starting behaviour and average word count the bar charts

above seem to confirm the notion that more threads are started when influence is highest

and word count increases. However, this user had a relatively constant posting

behaviour; thus it is only with thread starting behaviour a clear difference can be seen.

4.2.2.2. Kay

Figure 4.16. Chart displaying posting behaviour from 2008 – 2011 for LWP member

Kay.

0

10

20

30

40

50

2005 2006 2007 2008 2009 2010 2011

Year

Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

0

100

200

300

400

2008 2009 2010 2011To

tal

Nu

mb

er

of

Po

sts

Year

Total Number of Posts (Per Year)

251

Figure 4.17. Chart displaying total number of thread started each from 2008 – 2011 for

LWP member Kay.

Figure 4.18. Chart displaying total average word count for threads and posts each year

from 2008 – 2011 for LWP member Kay.

Synopsis of Kay

The above graphs illustrating posting, thread starting and word count appear to

correspond with the notion that Skinhead was influential in early part of the cluster

around December 2009. Equally, there is a clear drop in posting and thread starting

behaviour, which corresponds with a change in social role. This suggests that this LWP

member posted more and started more threads when categorised as influential.

However, between December 2010 and June 2011 Kay rose to status once again, but

little evidence of a dramatic difference in the posting and thread starting behaviour as

0

2

4

6

8

2008 2009 2010 2011To

tal

Nu

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er

of

Th

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ds

Sta

rte

d

Years

Total Number of Threads Started (Per Year)

0

200

400

600

800

2008 2009 2010 2011

Year

Average Word Count (Per Year)

Average Post Word Count Average Thread Word Count

252

presented in the graphs above. Nonetheless, there is a slight increase from the

behaviour displayed in 2010.

4.4.2.3. Comrade Believer

Figure 4.19. Chart displaying posting behaviour from 2002 – 2011 for LWP member

Comrade Believer.

Figure 4.20. Chart displaying total number of thread started each from 2002 – 2011 for

LWP member Community Believer.

0

50

100

150

200

250

2002 2003 2004 2005 2006 2007 2008 2009 2010

To

tal

Nu

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of

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Total Number of Posts (Per Year)

0

5

10

15

20

2002 2003 2004 2005 2006 2007 2008 2009 2010

To

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Total Number of Threads Started (Per Year)

253

Figure 4.21. Chart displaying average word count for threads and posts each year from

2002 – 2011 for LWP member Comrade Believer.

Synopsis of Community Believer

While the cluster analysis only illustrates status between 2009 – 2011, the above graph

dates back to Community Believers entry into the community. As illustrated above,

there is a high level of both thread starting, posting behaviour and an increased word

count in 2004-2005. Equally, there is a high level of activity within the community in

2009 when this LWP community member was influential within the community. The

decrease in 2010 corresponds with a transition to contributor.

4.4.2.4. Brothering

0

50

100

150

200

250

300

2003 2004 2005 2006 2007 2008 2009 2010

Year

Average Word Count (Per Year)

Average Post Word Count Average Thread Word Count

0

100

200

300

400

500

2008 2009 2010 2011To

tal

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Total Number of Posts (Per Year)

254

Figure 4.22. Chart displaying posting behaviour from 2008 – 2011 for LWP member

Brothering.

Figure 4.23. Chart displaying total number of thread started each from 2008 – 2011 for

LWP member Brothering.

Figure 4.24. Chart displaying average word count each year from 2008 – 2011 for LWP

member Brothering.

Synopsis of Brothering

Interestingly, the information displayed in the charts above for thread starting frequency

does not necessarily correspond with the cluster analysis. Between December 2010

and June 2011 Brothering was categorised as an influential. Yet, posting behaviour and

thread starting behaviour are highest in 2008. However, there is an increase in posting

behaviour during 2011, which may suggest posting behaviour is related to status and

0

5

10

15

20

25

2008 2009 2010 2011

To

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Sta

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Total Number of Threads Started (Per Year)

0

50

100

150

2008 2009 2010 2011

Year

Average Word Count (Per Year)

Average Post Word Count Average Thread Word Count

255

influence. Nonetheless, word count for threads and posts does correspond with social

status between 2010 – 2011.

LWP Members that Gained Reputation over a Two-Year Period

Black Magician

24 - 18 months 6 - 0 months

24-18 months 6-0 months

Betweenness Centrality 13776.495 12717.185

PageRank 5.906 5.371

Figure 4.26. Two social network analysis diagrams for online community member

“Black Magician” taken over a two-year period from November 2009 – November

2011.

Both of these figures are taken from the cluster analysis when this user was categorised

as an influential individual and shows a continuous level of centrality and connectivity.

Guy Fawkes

18 - 12 months 12 - 6 months

256

18-12 months 12-6 months

Betweenness Centrality 731.029

8976.702

PageRank 1.749 4.818

Figure 4.27. Two social network analysis diagrams for online community member

“Guy Fawkes” taken over a period of one year from May 2010 – November 2010.

The diagram on the left is taken from when this member was categorised as a

collaborator and the diagram on the right is taken from the cluster analysis when this

user was categorised as an influential individual.

Community Member

18 - 12 months 6 - 0 months

257

18-12 months 6-0 months

Betweenness Centrality 10889.218 44854.614

PageRank 2.338 7.645

Figure 4.28. Two social network analysis diagrams for online community member

“Community Member” taken over an 18 month period of one year from May 2010 –

November 2011.

The diagram on the left is taken from the cluster analysis when this online community

member was categorised as a collaborator. Alternatively, the diagram on the right is

taken when this LWP member was categorised as an influential individual with the

community.

Guerrilla Warfare

24 - 18 months 18 - 12 months

258

24-18 months 18-12 months

Betweenness Centrality 4096.360 1194.875

PageRank 3.950 1.882

Figure 4.29. Two social network analysis diagrams for online community member

“Guerrilla Warfare” taken over a one year period November 2009 – November 2010.

The diagram on the left illustrates when “Guerrilla Warfare” was categorised as an

influential member of the community via the cluster analysis. Moreover, the diagram

on the right demonstrates a change in social roles and centrality within the network

when this member was categorised as a collaborator. This user was banned for the

remainder of the cluster analysis.

LWP Members that Lost Reputation over a Two-Year Period

Kay

12 – 6 months 6 – 0 months

259

12-6 months 6-0 months

Betweenness Centrality 15972.326 0.369

PageRank 4.624 0.297

Figure 4.30. Two social network analysis diagrams for online community member

“Kay” taken over a one year period November 2010 – November 2011.

The diagram on the left is taken from when the cluster analysis categorised “Kay” as

an influential community member. Alternatively, the diagram on the right illustrates a

change in potential category to collaborator.

Comrade Believer

24 – 18 months 12 – 6 months

260

24-18 months 12-6 months

Betweenness Centrality 1982.298 0.000

PageRank 3.330 0.170

Figure 4.31. Two social network analysis diagrams for online community member

“CommunityBeliever” taken over 18 month period November 2009 – May 2011.

The diagram on the left is taken when the cluster analysis categorised

“CommunityBeliever” as an influential member within the community and the diagram

on the right shows the network structure for CommunityBeliever when the cluster

analysis deemed this member to be a contributor.

Skinhead

12 - 6 months 6-0 months

261

Figure 4.32. Two social network analysis diagrams for online community member

“Skinhead” taken over a one year period November 2010 – November 2011.

12-6 months 6-0 months

Betweenness Centrality 9972.139 383.367

PageRank 3.201 1.218

The diagram on the left shows LWP member “Skinhead” when he was influential

within the community and the diagram to the right shows a change in social role to

collaborator.

Brothering

18 – 12 months 12 – 6 months

262

18-12 months 12-6 months

Betweenness Centrality 46.671 4028.632

PageRank 0.426 3.629

Figure 4.33. Two social network analysis diagrams for online community member

“Skinhead” taken over a one year period May 2010 – May 2011.

The diagram on the left illustrates “Brothering” when he was a collaborator within the

community and the diagram on the right shows a change in social role to influential

status 6 months later.

263

4.8.1. Individual Synopsis for Eight Islamic United Members

4.8.1.1.Intoodeep

Figure 4.38. Chart displaying total number of posts per year from 2006 – 2011 for IU

community member Intoodeep.

Figure 4.39. Chart displaying total number of threads started per year from 2006 –

2011 for IU community member Intoodeep.

0

50

100

150

200

250

300

350

400

2006 2007 2008 2009 2010 2011

To

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Total Number of Posts (Per Year)

0

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2006 2007 2008 2009 2010 2011

To

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Total Number of Threads Started (Per Year)

264

Figure 4.40. Chart displaying average word count for threads and posts each year

from 2006 – 2011 for IU community member Intoodeep.

Synopsis of Intoodeep

The charts above appear to correspond with the notion that Intoodeep was an influential

member of the IU community in 2011. Equally, there is a relatively steady increase of

posts, threads and average word count leading upon a peak in 2011.

4.8.1.2. Gag Order

Figure 4.41. Chart displaying total number of posts per year for from 2006– 2011 for

IU community member Gag Order.

0

100

200

300

400

500

600

700

2006 2007 2008 2009 2010 2011

Av

era

ge

Wo

rd C

ou

nt

Years

Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

0

50

100

150

200

250

300

350

400

2006 2007 2008 2009 2010 2011

To

tal

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f P

ost

s

Year

Total Number of Posts (Per Year)

265

Figure 4.42. Displaying total number of threads started per year for from 2006 – 2011

for IU community member Gag Order.

Figure 4.43. Displaying average word count each year from 2006 – 2011 for IU

community member Gag Order.

Synopsis for Gag Order

According to the cluster analysis Gag Order was influential within the IU community

throughout 2011; however, the average word count per posts and threads does not

appear to correspond with the notion that individuals have a higher word count when

0

5

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15

20

2006 2007 2008 2009 2010 2011

To

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Total Number of Threads Started (Per Year)

0

50

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200

2006 2007 2008 2009 2010 2011

Years

Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

266

influential within a community. Nonetheless, thread starting behaviour and posting

behaviour does peak when status was gained within the community.

4.8.1.3. Al-Omari

Figure 4.44. Chart displaying average word count each year from 2006 – 2011 for IU

community member Gag Order.

Figure 4.45. Chart displaying total number of threads per year for from 2007 – 2011

for IU community member Al-Omari.

0

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1000

2007 2008 2009 2010 2011

To

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6

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12

14

2007 2008 2009 2010 2011

To

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Total Number of Threads Started (Per Year)

267

Figure 4.46. Displaying average word count for threads and post each year from 2007

– 2011 for IU community member Al-Omari.

Synopsis for Al-Omari

The cluster analysis for Al-Omari states that this user was influential in 2011, which

appears to parallel the posting behaviour and thread starting behaviour as exhibited in

the charts above. However, interestingly, the average word count behaviour does not

peak in 2011 and stays relatively consistent throughout his time in the community.

4.8.1.4. Pluma

0

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Av

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Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

0

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1200

1400

2008 2009 2010 2011

To

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Total Number of Posts (Per Year)

268

Figure 4.47. Chart displaying total number of posts per year for from 2008 – 2011 for

IU community member Pluma.

Figure 4.48. Chart displaying total number of threads per year for from 2008 – 2011

for IU community member Pluma.

Figure 4.49. Chart displaying average word count for threads and posts each year

from 2008 – 2011 for IU community member Pluma.

Synopsis for Pluma

0

2

4

6

8

2008 2009 2010 2011

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500

1000

1500

2000

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2008 2009 2010 2011

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Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

269

Posting behaviour and thread starting behaviour for IU member Pluma coincides with

that of the cluster analysis, supporting the notion that thread starting behaviour and

posting behaviour increases with influence. However, average word count peaks in

2011; while Pluma is still categorised as influential at this time, one would expect to

see an increase in 2009 when posts and threads were at their highest.

4.8.1.5.Weshallnotkeepsilent

Figure 4.50. Chart displaying total number of posts per year for from 2010 – 2011 for

IU community member Weshallnotkeepsilent.

0

50

100

150

200

250

2010 2011

To

tal

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Total Number of Posts (Per Year)

0

5

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15

20

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2010 2011

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270

Figure 4.51. Chart displaying total number of threads per year for from 2010 – 2011

for IU community member Weshallnotkeepsilent.

Figure 4.51. Chart displaying average word count for threads and posts each year

from 2010 – 2011 for IU community member Weshallnotkeepsilent.

Synopsis for Weshallnotkeepsilent

Interestingly, both the thread starting behaviour and posting behaviour is consistent

with the notion that Weshallnotkeepsilent was influential in 2010. However, the

average word count for threads dramatically increases in 2011, which is not consistent

with the previous charts or the cluster analysis. There is no other information for this

user.

0

100

200

300

400

500

600

700

2010 2011

Av

era

ge

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Years

Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

271

4.8.1.6. Free the Oppressed

Figure 4.53. Chart displaying total number of posts per year for from 2009 – 2010 for

IU community member Free the Oppressed.

Figure 4.54. Displaying total number of threads per year for from 2009 – 2010 for IU

community member Free the Oppressed.

0

100

200

300

400

500

600

700

800

2009 2010

Su

m o

f P

ost

s

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Total Number of Posts (Per Year)

0

20

40

60

80

100

2009 2010

Su

m o

f T

hre

ad

s

Years

Total Number of Threads Started (Per Year)

272

Figure 4.55. Chart displaying total number of posts per year for from 2009 – 2010 for

IU community member Free the Oppressed.

Synopsis for Free the Oppressed

All of the charts above appear to support each other and the notion that Free the

Oppressed was influential between December 2009 – December 2010. Equally,

average word count, threads starting behaviour and posting behaviour all peaked at the

same point in 2010. There is no more available information after this point as Free the

Oppressed became inactive in the community after this point.

4.8.1.7. Umar247

0

200

400

600

800

2009 2010

Years

Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

0

100

200

300

400

500

600

2008 2009 2010 2011

To

tal

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Years

Total Number of Posts (Per Year)

273

Figure 4.56. Chart displaying total number of posts per year for from 2008 – 2011 for

IU community member Umar247.

Figure 4.57. Chart displaying total number of threads per year for from 2008 – 2011

for IU community member Umar247.

Figure 4.58. Chart displaying average word count each year from 2008 – 2011 for IU

community member Umar247.

0

10

20

30

40

50

60

70

80

2008 2009 2010 2011

To

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Total Number of Threads Started (Per Year)

0

100

200

300

400

500

600

700

800

2008 2009 2010 2011

Years

Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

274

Synopsis for Umar247

According to the cluster analysis, this individual community member was deemed to

be influential in 2009-2010. Posting behaviour appears to be highest at this point in

time suggesting that frequent posting is a factor involved within high ranking status.

Thread starting behaviour was also quite high at this point too. Interestingly, average

word count does not appear to follow this pattern as exhibited in the charts above.

While average thread word count is considerably higher than average post word count,

both have a relatively stable frequency, regardless of influence or status., suggesting

longer posts and threads may not be a factor in high-ranking status.

4.8.1.8. Iqra

Figure 4.59. Chart displaying total number of posts per year for from 2006 – 2011 for

IU community member Iqra.

0

20

40

60

80

100

120

140

160

180

2006 2007 2008 2009 2010 2011

To

tal

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Total Number of Posts (Per Year)

275

Figure 4.60. Chart displaying total number of threads per year for from 2006 – 2011

for IU community member Iqra.

Figure 4.61. Chart displaying total number of threads per year for from 2006 – 2011

for IU community member Iqra.

Synopsis for Iqra

The above cluster analysis only ranges from December 2009-2011, therefore, one

cannot determine whether Iqra was influential in 2008/2009 or not. However, posting

0

5

10

15

20

25

2006 2007 2008 2009 2010 2011

To

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Total Number of Threads Started (Per Year)

0

100

200

300

400

500

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700

2006 2007 2008 2009 2010 2011

Years

Average Word Count (Per Year)

Average Word Count Posts Average Word Count Threads

276

behaviour and thread starting behaviour is highest in 2009 suggesting a somewhat high

ranking social status. According to the above cluster analysis, Iqra was influential in

2010, however, the above graphs do not necessarily coincide with this information.

4.8.2. Social Network Analysis for Islamic United

As mentioned in the LWP part of this chapter, the social network graphs

illustrate social structure and visually demonstrate how connected and central each of

the eight individuals are within their respected community. As such, the diagrams

below reveal social transitions and changes in structure over a two-year period. As

alluded to previously, the nodes represent other members and the lines are the

connections or social ties illustrating communication between the two members. The

more ties an individual had (or influential ties) the higher their social status within a

community.

4.8.2.1. IU Members that Gained Reputation over a Two-Year Period

Gag Order

24 - 18 months 6 - 0 months

24-18 months 6-0 months

Betweenness 543.240 9129.061

277

PageRank 2.169 6.385

Figure 4.62. Two social network analysis diagrams for user “Gag Order” taken over

a two-year period from December 2009 – December 2011.

Far left graph is taken from 24-18 months cluster analysis when “Gag Order” was

categorised as a collaborator within the community and graph on the right is the most

recent cluster analysis from 6-0 months when this user was deemed to be an

influential individual/popular participant within IU.

Intoodeep

24 - 18 months 6 - 0 months

24-18 months 6-0 months

Betweenness 4837.805 2115.090

PageRank 4.492 3.250

Figure 4.63. Two social network analysis diagrams for user “Intoodeep” taken over a

two-year period from December 2009 – December 2011.

278

The graph on the right demonstrates community member “Intoodeep’s” connectivity

and social structure when he was a contributor within the community. Alternatively,

the second diagram shows this user when he was an influential individual within IU.

Pluma

24 - 18 months 6 - 0 months

24-18 months 6-0 months

Betweenness 20651.617 5126.101

PageRank 9.052 4.597

Figure 4.64. Two social network analysis diagrams for user “Pluma” taken over a

two-year period from December 2009 – December 2011.

The network graph on the left shows “Pluma” when he was a collaborator within the

online community IU and the graph on the right shows the same user 2 years later,

when they had supposedly gained status and influence within the online community

(based on the cluster analysis).

Al-Omari

279

24 - 18 months 6 - 0 months

24-18 months 6-0 months

Betweenness 848.534 4480.231

PageRank 1.810 4.596

Figure 4.65. Two social network analysis diagrams for user “Al-Omari” taken over a

two-year period from December 2009 – December 2011.

The network graph on the left shows “Al-Omari’ when he was a contributor within

the IU community. The graph on the right shows this user two years later when they

became influential.

4.7.2.2. IU Members that Lost Reputation over a Two-Year Period

280

Umar 247

18 - 12 months 6 - 0 months

18-12 months 6-0 months

Betweenness 4377.714 1074.166

PageRank 5.127 2.903

Figure 4.66. Two social network analysis diagrams for user “Umar247” taken over an

18 month year period from June 2010 – December 2011.

The graph on the left shows “Umar247” when he was influential within the

community, alternatively, the second diagram illustrates a loss in connectivity 18

months later when they were demoted to the lesser status of collaborator.

281

Free the Oppressed

24 - 18 months 6 - 0 months

24-18 months 6-0 months

Betweenness 11952.395 0.000

PageRank 7.023 0.197

Figure 4.67. Two social network analysis diagrams for user “Free the Oppressed”

taken over a two-year period from December 2009 – December 2011.

As illustrated by the above diagrams, at 24-18 months community member “Free the

Oppressed” was highly influential within the IU community, though became almost

entirely inactive two years later.

282

Iqra

24 - 18 months 6 - 0 months

24-18 months 6-0 months

Betweenness 0.000 2.017

PageRank 0.208 0.319

Figure 4.68. Two social network analysis diagrams for user “Iqra” taken over a two-

year period from December 2009 – December 2011. No info from when she was a

‘leader’

“Iqra” was an influential member in the 18-12 month cluster analysis; however there

is no information to illustrate the social network structure for this period.

Nonetheless, the second diagram supports the notion that “Iqra” was almost entirely

inactive at the 6-0 month analysis and had lost status within the online community.

283

Weshallnotkeepsilent

18 - 12 months 6 - 0 months

18-12 months 6-0 months

Betweenness 5111.670 845.981

PageRank 3.347 2.423

Figure 4.69. Two social network analysis diagrams for user “Weshallnotkeepsilent”

taken over an 18 month period from June 2010 – December 2011.

The network graph on the left illustrates “Weshallnotkeepsilents’s” social network

structure and connectivity at 18-12 months. The second graph shows the same user

18 months later when they had lost some of their status within the community and

made the role transition from influential to collaborator.

284

Inferential Statistics LWP

Table 4.9.

Summary of ANOVA

df SS MS F η2 p

Seniority 3 89036527.92 29678842.64 2.792* .075 .044*

Total Post

Word Count

3 5.264E+11 1.755E+11 3.606* .095 .016*

Word Count

(per post)

3 1973.796 657.932 .392 .011 .759

Reputation 3 216021652.6 72007217.52 18.910 .355 .000**

Number of

Thanks

3 29788114.47 9929371.490 15.913 .317 .000**

Thread

Total

3 27966.798 9322.266 3.296 .088 .023*

Total

Thread

Word Count

3 1494058712 498019570.6 2.722 .073 .048*

Word Count

(per Thread)

3 162875.708 54291.903 .392 .006 .899

Average

Number of

Thanks

(per post)

3 4490.945 1496.982 2.913 .078 .038*

Note. * indicated findings that are significant at the p<0.05 level, ** illustrates

significance at the p<0.001 level.

Table 4.10.

Multiple Comparisons and Mean Differences in Status for the Significant Dependent

Variables

285

Note. * indicated findings that are significant at the p<0.05 level, ** illustrates

significance at the p<0.001 level.

Table 4.12

Estimates of Fixed Effects for Model for Group Status*Month Thread Frequency

Parameter Estimate Std.

Error

Df t p 95% Confidence

Interval

Intercept

.024276 .006232 2546.000 3.895 .000** .012054 .036497

Dependent

Variable

Comparison Mean

Difference

p Games-Howells

95% CI

Seniority Always PP vs PP Demoted 1988.91 .011* 362.04, 3615.78

Word Count

for Posts

Always PP vs PP Demoted 153099.14 .001* 52299.52,

253898.75

Always PP vs Promoted PP 153170.99 .012* 26498.75,

279843.24

Reputation Always PP vs PP Demoted 2672.60 .000** 1632.21, 3712.99

Always PP vs PP Inactive 2937.75 .002* 982.65, 4892.85

Always PP vs Promoted PP 3184.58 .000** 2159.16, 4209.99

Number of

Thanks

Always PP vs PP Demoted 1144.45 .000** 658.11, 1630.80

Always PP vs PP Inactive 999.66 .000** 435.72, 1563.61

Always PP vs Promoted PP 1022.25 .000** 512.12, 1532.36

Thread

Count

Always PP vs PP Demoted 35.89 .013* 5.93, 65.85

286

Always

PP

07 0 . . . .

PP

Demoted -.04908 .009067 2546 -5.414 .000** -.066863 -.031305

PP

Inactive -.066345 .011968 2546.000 -5.544 .000** -.089813 -.042877

Promoted

to PP

-.012240 .009021 2546 -1.357 .175 -.029929 .005449

Note. As Always PP is the baseline group the incept value of .024276 represents the

mean thread frequency for Always PP.

Table 4.13.

Pairwise Comparisons for Group Status and Thread Frequency

Comparison Mean

Difference

p 95% Confidence Interval

Always PP vs Promoted 2.200* .000 2.038 2.362

Always PP vs Demoted 2.107* .000 1.948 2.267

Always PP vs PP

Inactive

2.084* .000 1.899 2.270

Table 4.15.

7 Always PP is set to zero as this is the baseline for status groups to assess these intercepts.

As SPSS automatically choses the last category groups were recoded to allow for this

meaningful comparison.

287

Estimates of Fixed Effects for Model for Group Status*Month Post Frequency

Parameter Estimate Std.

Error

df t p 95% Confidence

Interval

Intercept 1.095608 .087970 2546.000 12.454 .000**

.923109

Always PP 0b 0 . . .

.

PP Demoted

-2.323113 .127978 2546 -18.152 .000** -2.574065 -2.072161

PP Inactive

-1.878023 .168926 2546.000 -11.117 .000** -2.209268 -1.546777

Promoted to

PP -.430358 .127330 2546.000 -3.380 .001* -.680038 -.180677

Table 4.16.

Pairwise Comparisons for Group Status and Post Frequency

Comparison Mean

Difference

p 95% Confidence Interval

Always PP vs Promoted PP

73.139* .000** 71.853 76.425

Always PP vs Demoted 69.150* .000** 66.898 71.402

PP Demoted vs PP Inactive 66.940* .000** 64.319 69.561

Promoted to PP vs PP

Inactive

-7.199 .000** -9.869 -4.529

288

Promoted to PP vs PP

Demoted

-4.989

.000** -7.298 -2.680

Table 4.18.

Estimates of Fixed Effects for Model for Group Status*Month Number of Thanks

Frequency

Parameter Estimate Std.

Error

df t p 95% Confidence

Interval

Intercept 1.389133 .044865 2546.000 30.962 .000** 1.301156 1.477109

Always PP 08 0 . . . .

PP Demoted

-1.646879 .065270 2546 -25.232 .000** -1.774867 -1.518892

PP Inactive

-1.455835 .086154 2546.000 -16.898 .000** -1.624774 -1.286897

Promoted to

PP -1.032589 .064939 2546.000 -15.901 .000** -1.159928 -.905250

Note. As Always PP is the baseline group the intercept value of 1.389133 represents

the mean number of thanks for Always PP.

Table 4.19.

Pairwise Comparisons for Group Status and Number of Thanks Frequency

8 Always PP is set to zero as this is the baseline for status groups to assess these intercepts.

SPSS automatically choses the last category. Thus, groups were recoded to allow for this

meaningful comparison.

289

Comparison Mean

Difference

p 95% Confidence Interval

Always PP vs PP Inactive 28.539 0.000** 27.202 29.876

Always PP vs Demoted 29.162 0.000** 28.013 30.310

Always PP vs Promoted PP 30.138 0.000** 28.973 31.304

Promoted to PP vs PP Inactive -1.599 .012* -2.961 -.237

Table 4.21

Estimates of Fixed Effects for Model for Group Status*Month Number of Thanks

Frequency

Parameter Estimate Std.

Error

Df t p 95% Confidence

Interval

Intercept -.124935 .011555 2368 -10.812 .000** -.147594 -.102275

Always PP 0 0 . . . .

PP

Demoted -.051547 .017268 2368 -2.985 .003* -.085408 -.017685

PP

Inactive -.009758 .022647 2368.000 -.431 .667 -.054168 .034653

Promoted

to PP .090186 .018099 2368.000 4.983 .000** .054696 .125677

Note. Always PP is the baseline group the incept value of -.124935 represents the mean

number of thanks for Always PP.

Table 4.22

Pairwise Comparisons for Group Status and Average Number of Thanks Frequency

Comparison Mean Difference p 95% Confidence Interval

Always PP vs PP Inactive -.719 .000 -1.074 -.364

Always PP vs Promoted PP .353 .024 .030 .676

290

PP Demoted vs PP Inactive -.508 .001 -.871 .001

PP Demoted vs Promoted to

PP

.564

.000

.233

.896

PP Inactive vs Promoted to

PP

1.072 .000 .691 1.453

Inferential Statistics IU

Table 4.30

Summary of ANOVA

df SS MS F η2 p

Seniority 3 85845924.92 28615308.31 5.343 .334 .004*

Post Word

Count

3 2932895420 977631806.7 1.707 .138 .185

Average Post

Word Count

3 328400.598 9466.866 5.328 .333 .004*

Reputation 3 164632777.8 54877592.59 10.545 .497 .000**

Number of

Thanks

3 2618962.633 872987.544 7.294 .406 .001*

Thread Total 3 31314.367 10438.122 1.807 .145 .166

Thread Word

Count

3 5305109844 1768369948 .611 .054 .613

Average

Thread Word

Count

3 211179.191 70393.064 .662 .582 .058

Average

Number of

Thanks

3 70127.642 23375.881 .980 .084

.415

Note. * indicated findings that are significant at the p<0.05 level, ** illustrates

significance at the p<0.001 level.

291

Table 4.31

Multiple Comparisons and Mean Differences in Status for the Significant Dependent

Variables

Note. Findings are significant at the p<0.05 level.

Table 4.33

Estimates of Fixed Effects for Model for Group Status*Month Thread Frequency

Dependent

Variable

Comparison Mean

Difference

p Games-Howells

95% CI

Seniority Always PP vs PP Demoted 3285.17 .018* -1564.96, 8135.02

Always PP vs Inactive 3905.67 .006* -960.55, 8771.88

PP Inactive vs Promoted to PP -2661.56 .074* -8276.38, 2953.27

Reputation Always PP vs PP – Demoted 4800.00 .005* -1044.92, 10644.92

Always PP vs PP Inactive 5150.00 .003* -711.26, 11011.26

Always PP vs Promoted to PP 3405.56 .046* -2353.71, 9164.82

PP Demoted vs PP Inactive 350.00 .038* -279.11, 979.11

PP Demoted vs Promoted PP -1394.44 0.38* -3901.80, 1112.91

PP Inactive vs Promoted to PP -1744.44 .012* -4342.73, 853.84

Number of

Thanks

Always PP vs PP Demoted 587.68 .009* -198.59, 1373.96

Always PP vs PP Inactive 656.88 .004* -133.87, 1447.64

PP Demoted vs PP Inactive 69.20 .038* -55.53, 193.93

PP Demoted vs Promoted to PP -379.60 .049* -1112.42, 353.22

Promoted to PP vs PP Inactive 448.80 .021* -1195.69, 298.09

Average Post

Word Count

Always PP vs PP Inactive 64.0162 .036* 3.9965, 124.0359

292

Parameter Estimate Std.

Error

df t p 95%

Confidence

Interval

Intercept .588886 .067656 2270 8.704 .000 .456212 .721559

Always

PP

09 0 . . . .

PP

Demoted -.030823 .007002 2270 -4.402 .000 -.044554 -.017093

PP

Inactive -.054583 .020534 2270 -2.658 .008 -.094850 -.014316

Promoted

to PP -.010746 .006558 2270 -1.638 .101 -.023607 .002115

Note. As Always PP is the baseline group the incept value of .588886 represents the

mean month*status thread frequency for Always PP.

Table 4.34

Pairwise Comparisons for Group Status and Thread Frequency

Comparison Mean

Difference

p 95% Confidence Interval

Promoted to PP vs PP Demoted .433* .000** .303 .563

Promoted to PP vs PP Inactive .427* .023* .038 .817

Always PP vs PP Demoted .500* .000** .369 .630

Always PP vs PP Inactive .494* .005* .104 .884

Table 4.36.

Estimates of Fixed Effects for Model for Group Status*Month Number of Posts

Frequency

Parameter Estimate Std. Error df t P 95% Confidence Interval

9 Always PP is set to zero as this is the baseline for status groups to assess these

intercepts. As SPSS automatically choses the last category groups were recoded to

allow for this meaningful comparison.

293

Intercept 8.286013 .806825 2270.000 10.270 .000 6.703821 9.868205

Always PP 0 0 . . . .

PP Demoted -1.035868 .083500 2270 -12.406 .000 -1.199612 -.872124

PP Inactive -1.215556 .244876 2270.000 -4.964 .000 -1.695760 -.735352

Promoted to

PP

-.906747 .078212 2270 -11.593 .000 -1.060121 -.753372

Table 4.39.

Estimates of Fixed Effects for Model Group Status*Month for Number of Thanks

Frequency

Parameter Estimate Std. Error df t p 95% Confidence

Interval

Intercept .641972 .020224 2270 31.742 .000 .602311 .681632

Always PP 0 0 . . . .

PP Demoted -.556892 .030559 2270.000 -18.223 .000 -.616819 -.496965

PP Inactive -.642682 .089620 2270.000 -7.171 .000 -.818427 -.466937

Promoted to

PP -.231482 .028624 2270 -8.087 .000 -.287614 -.175350

Table 4.40

Pairwise Comparison for Group Status and Monthly Number of Thanks

Comparison Mean

Difference

p 95% Confidence

Interval

Always PP vs Promoted 1.219* .000** .680 1.758

Always PP vs PP

Demoted 2.929* .000** 2.359 3.498

Always PP vs PP

Inactive 3.340* .000** 1.640 5.041

Promoted to PP vs PP

Demoted 1.710* .000** 1.141 2.279

294

Promoted to PP vs PP

Inactive 2.122* .006** .421 3.822

Table 4.42

Estimates of Fixed Effects for Model for Group Status*Month Average Number of

Thanks Frequency

Parameter Estimate Std. Error df t p 95% Confidence Interval

Intercept -1.176069 .065679 682 -17.906 .000 -1.305027 -1.047111

Always PP 0 0 . . . .

PP Demoted .956235 .164731 682.000 5.805 .000 .632794 1.279677

PP Inactive 1.551069 1.530864 682 1.013 .311 -1.454704 4.556842

Promoted to

PP -.155648 .106459 682.000 -1.462 .144 -.364675 .053378

Table 4.43

Pairwise Comparison for Group Status and Monthly Average Number of Thanks

Comparison Mean

Difference

p 95% Confidence

Interval

Promoted to PP vs PP

Demoted 3.872* .000** 2.005 5.739

Always PP vs PP

Demoted 3.770* .000** 1.936 5.603

Table 4.37.

Pairwise Comparisons for Group Status and Post Frequency

Comparison Mean

Difference

p 95% Confidence Interval

295

Always PP vs PP Demoted 14.890* .000** 13.335 16.446

Always PP vs PP Inactive 16.627* .000** 11.980 21.274

Promoted to PP vs PP Demoted 15.820* .000** 14.266 17.375

Promoted PP vs PP Inactive 17.557* .000** 12.911 22.203

296

0Appendix 5: Sample Theme Table for Phase Two

RESTRICTED ACESS