Is Friend Status a Social Network Tie?Laura Louise Patersonl.l.paterson@leeds.ac.uk

School of EnglishFACULTY OF ARTS

The Milroy’s developed the concept of social networks and the notion of strong and weak ties

They infiltrated communities in Belfast and observed whether social ties correlated with linguistic norms

They found that those outside the core group, and not those within it, could be linguistic innovators

They argued for two main types of social networksStrong versus Weak networksDense versus Loose networks

The social networks of social networks

Dense social networks

In the example A knows everyone, B knows everyone, C knows everyone, and so on

There are lots of complete triangles

But it’s not as simple as this…

Strong networks

People don’t have just one tie:Work together, live near each other, socialise together, go to school together, etc.

Multiple ties (multiplex)

We don’t all have strong bonds with everyone we know

Not everyone knows each other

Different bonds might be different strengths

Looser bonds = less close-knit

Loose/weak networks

We have both strong and weak tiesWe have multiple ties

Most networks are a mixture

Taken from Milroy and Milroy (1997:201)

Theories of language changeLinguistic innovations will diffuse through networks using weak ties

Close-knit networks may adopt innovations if the change reaches the network innovator

This is generally a person within the close-knit network who also has loose network ties to other close-knit networks

B and F are innovators in their own networks due to their weak tie with each otherA

BC

D

EF

Can this model of social networks, even though it is from the days before Facebook, be applied to the social networks we have online?

Is there a way to map out your Facebook friends into these models?

More importantly for us, could you trace the use of a linguistic variable through these networks?

What about other online SNS like Twitter and Flikr and Pinterest and Beebo?

My questions…

Facebook tiesFacebook networks aid the “bridging of social capital”, which Hsu, Wang and Tai (2011:437) argue is “closely linked to the notion of ‘weak’ ties”.

They note that “the literature does not examine the individuals with whom users would have strong ties and weak ties or how users maintain such ties on Facebook” (Hsu, Wang and Tai 2011:473). So the jury is out. But we have to consider Lewis et al.’s (2008:332) argument that “All friendships are indistinguishable with respect to tie strength, and informal reports from Facebook users… suggest that users enter these relationships rather casually”.

But this depends on how terms like ‘casually’ are defined.

What is a Facebook friend?

Tokunaga (2011:426) notes “The ambiguous and elastic notion of ‘‘friends’’ on SNSs” and argues that the term “obscures the nature of the relationships between users”.

“friends on SNS are not always defined in the traditional sense – some users connect to celebrities and bands they do not know personally” (Zhong, Hardin and Sun 2011:1266).

Problem 1: What is a Facebook friend?

But what do we know already? (Raacke and Bonds-Raacke 2008:171)

96% use Facebook/MySpace to keep in contact with old friends91.1% use Facebook/MySpace to keep in contact with current

friends56.4% To make new friends 54.5% To find old friends

Facebook survey: 397 people conducted online in early 2012

Average age of participants 20-21

Females 66%, Males 30%, No answer 4%

Respondents taken mainly from northern England, but survey was open UK wide

Thank you to my Language Online classes of 2011/2012

Facebook as a social network

Facebook is social media

0

50

100

150

200

250

300

350

400

450

Yes NoI used to but I don't anymore

No answer

Do you have profiles on any of the following websites?

92.91% respondents logged on to Facebook at least once a day

39.54% used Facebook chat at least once a day

68.76% used Facebook chat at least once a week

Lots of people are using this tool for communication and producing language samples in the process

Although we could also look at Facebook multimodally, I am focusing on linguistic features

It is a well-used resource

A sample networkWe can map Facebook networks using Gephi (gephi.org)99 nodes (people) 329 edges (links)6 colours = 6 communitiesBetweenness centrality: nodes people pass through to communicate 1. Partner > 2. Friend 15 years > 3. Friend/colleague 5 years > 4. Friend 8 years > 5. Mother > 6. Friend 10 years

Can we track a linguistic feature? Example 1

Correcting spelling with Mother (key figure)

Mother: I can’t believe it’s been thirty years since we watched you walk down the isle xxxMe: She means aisle peopleFB User: Aww I remember you walking down the isle from John O’Groats to Land’s End

Outcomes: Mother doesn’t edit her spellingNo obvious effect on other people’s

spellingAlthough… sometimes others react

Example 1 continued

This is problematic, but can we make it better?Could introduce a new form to the network and

trace its pathChance of innovation being taken up is

debateableMultiple experiments are needed

Facebook is a undirected network – all ties are mutualProblem 2: How we can weight the strength of online ties?

Frequency of interaction may help us here if we assume that it is more likely for users to interact with strong-ties than weak-ties over a set period of time

82.73% of respondents had over 200 Facebook friends0.76% had less than ten Facebook friends

But what type of network is it?

11.59

36.5225.44

15.37

6.55 0.25

Less than 10%Some 10-40%About Half 40-60%Most 60-80%Almost all 80-90%All 100%

How many Facebook friends do you see offline?

Having a lot of ties doesn’t automatically equal lots of interaction

How much communication?

60.20

28.72

4.53 1.76

Less than 10%Some 10-40%About Half 40-60%Most 60-80%Almost all 80-90%All 100%

How many people do you talk with via Facebook at least once a week?

Thinning down the networkEliminate nodes with limited contact31 nodes - 57 edges

Fewer communitiesFewer key nodes1. Partner > 2. Friend 15 years > 3. Friend/colleague 5 years >

Key persons 4 and 5 absorbed into Friend 15 years’ networkKey person 6 absorbed into Partner’s networkNo one absorbed into Friend/colleague’s networkDifferent types of network social vs. corporate explain this

Can we track a linguistic feature? Example 2

We can now identify three key social network linksDo their linguistic choices diffuse across the network?

P1: Partner > P2: Friend 15 years > P3: Friend/colleague 5 years

P1 and P2 are linkedP1 and P3 are linkedP2 and P3 are not linked – no direct transference of linguistic norms

BUT: Language could pass through P1:[innovation P3] [P1 does innovation] [P3 does

innovation]

BUT: P1 (potentially the most influential) uses Facebook very rarely

Thus chance for innovation spread decreases dramatically

Classifying participants

Brandtzæg (2012) identifies five different types of SNS users

1. Advanced users: Frequent use and diverse behaviour

2. Debaters: Upload content, write contributions and get into discussions about particular topics

3. Socialisers: Actively engaged with communicating with friends

4. Lurkers: Passive consumption of others’ data

5. Sporadics: Very close to non-users

1. Partner > 2. Friend 15

years > 3. Friend/

colleague 5 years >

4. Friend 8 years >

5. Mother > 6. Friend 10

years

Classifying participantsBrandtzæg (2012) identifies five different types of SNS users

1. Advanced users: Frequent use and diverse behaviour

2. Debaters: Upload content, write contributions and get into discussions about particular topics

3. Socialisers: Actively engaged with communicating with friends

4. Lurkers: Passive consumption of others’ data

5. Sporadics: Very close to non-users

1. Partner > 2. Friend 15

years > 3. Friend/

colleague 5 years > Problem 3: How

can we categorise all network participants in this way?

But one node is missing

The graph does not show my influence in the network

My network = I have high betweennessI am connected to every nodeInnovation by P3 could pass through me to

other nodesThe same is true for every other node

Therefore, everyone can influence language changeThe model becomes blurredProblem 4: Can the path of diffusion be calculated?

Where do we end the network?

The Gephi graph cannot show us whether two participants in my network are connected to a third party that I do not share

Problem 5: Where does the network end?Thus tracing network influence is going to be very difficultEspecially given the number of Facebook users

ME

P3P2 P2

X

P3

The key issuesIs online friend status a social network tie?

Yes, but its weight is uncertainNeed to test how different dense networks

in one overarching network interact with each other

Problem 1: What actually is a Facebook friend?Problem 2: How we can weight the strength of online ties?Problem 3: How can we categorise all network participants in this way?Problem 4: Can the path of diffusion be calculated?Problem 5: Where does the network end?

What is next?

Need to test more Facebook networks:My network could be anomalousAre there trends in the make up of Facebook

networks? Collection on-going

Case study: Three networks of varying sizesAre there common community structures?Is there a constant rate of decrease when

participants of limited/no contact are removed?

Does this work with the Milroys’ model?

Testing more networksBefore condensationNodes: 64Edges: 314 Av. Degree: 4.91

After condensationNodes: 30Edges: 149 Av. Degree: 4.99

Testing more networksBefore condensationNodes: 267Edges: 1166 Av. Degree: 4.37

After condensationNodes: 100Edges: 335 Av. Degree: 3.35

Testing more networksBefore condensationNodes: 483Edges: 11243 Av. Degree: 23.28

After condensationNodes: 58Edges: 214 Av. Degree: 3.69

The future…

Need to run more simulations on graphsNeed access to linguistic data

Need to move on to other SNS:Comparable software for mapping other networks

Questions?

References Baron, Naomi. 2008. Always On: Language in an Online and Mobile World. Oxford:

Oxford University Press. Brandtzæg, P. B. 2012. Social Networking Sites: Their Users and Social

Implications - A Longitudinal Study. Journal of Computer-Mediated Communication, 17, 467-488.

Donath J. and D boyd. 2004. Public displays of connection. BT Technology Journal 22: 71–82.

Ellison N.B., C. Steinfield and C. Lampe. 2007. The benefits of Facebook ‘‘friends’’: Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication 12: 1143–1168.

Ellison, N.B., C. Steinfield and C. Lampe. 2011. Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media & Society, 13: 873– 892.

Fono D. and K. Raynes-Goldie. 2005. Hyperfriends and beyond: Friendship and social norms on LiveJournal. In M. Consalvo and C. Haythornthwaite (eds.). 2005. Internet research annual: Selected papers from the Association of Internet Researchers Conference 2005. New York: Peter Lang.

Hampton, K.N., L.S. Goulet, L. Rainieand K. Purcell, K. 2011. Social networking sites and our lives: How people’s trust, personal relationships, and civic and political involvement are connected to their use of social networking sites and other technologies. <pewinternet.org/_/media//Files/Reports/2011/PIP%20-%20Social%20networking%20sites%20and%20our%20lives.pdf>

ReferencesHsu, C.W., C.C. Wang and Y.T. Tai. 2011. The Closer the Relationship,

the More the Interaction on Facebook? Investigating the Case of Taiwan Users. Cyberpsychology Behavior and Social Networking 14: 473-476.

Knapp M., D. Ellis, and B. Williams. 1980. Perceptions of communication behavior associated with relationship terms. Communication Monographs 1980 47: 262–78.

Lewis, K., J. Kaufman, M. Gonzalez, A. Wimmer and N. Christakis. 2008. Tastes, ties, and time: A new social network dataset using Facebook.com. Social Networks 30: 330-342.

Manago, A. M., T. Taylor and P.M. Greenfield. 2012. Me and My 400 Friends: The Anatomy of College Students' Facebook Networks, Their Communication Patterns, and Well-Being. Developmental Psychology 48: 369-380.

Milroy, James and Lesley Milroy. 1985. Authority in Language. London: Routledge and Kegan Paul.

Milroy, James, and Lesley Milroy. 1997. Network structure and linguistic change. In Adam Coupland and Nikolas Jaworski (Eds.) 1997. Sociolinguistics: A reader and coursebook. London: Palgrave Macmillan.

Raacke, J. and J. Bonds-Raacke, J. 2008. MySpace and facebook: Applying the uses and gratifications theory to exploring friend-networking sites. Cyberpsychology & Behavior 11: 169-174.

References Tokunaga, R. S. 2011. Friend Me or You'll Strain Us: Understanding

Negative Events That Occur over Social Networking Sites. Cyberpsychology Behavior and Social Networking 14: 425-432.

Zhong, B., M. HARDIN and T. SUN. 2011. Less effortful thinking leads to more social networking? The associations between the use of social network sites and personality traits. Computers in Human Behavior 27: 1265-1271.

Contact details:L.L.Paterson@leeds.ac.ukSchool of EnglishUniversity of LeedsLS2 9JT