Methodological Approaches in Information Science Research on Computer-mediated
Communication
Anis Pervez
Adjunct Faculty, Department of Media and Communication, Independent University Bangladesh (IUB),
Dhaka, Bangladesh;
Email id: [email protected].
Pearl: A Journal of Library and Information Science, Year: 2015, Volume: 9, Issue: 1, pp. 37-43
ABSTRACT
Computer-mediated communication (CMC) research in information science is progressively using
interpretive theories, which is also the case in the methods used in CMC research. Different of methods
are used including content analysis, discourse and hermeneutic analysis. With the fast diffusion of social
media, where people endorse meaning in different modes of information – text, image, sound – it is likely
that the use of different qualitative methods will increase.
KEYWORDS: CMC, Information science research, Qualitative method, Discourse analysis, Hermeneutic
analysis, Interpretivism, Social media
INTRODUCTION
Computer-mediated communication (CMC) artefacts are primarily text – linguistic and semiotic –
through which information is disseminated over a digital platform among the people who have access to
it. Access is both instrumental (having access to the Internet) and cognitive (extracting or constructing
meaning from the artefacts transferred by the net). CMC research in information science involves a
spectrum of theories as detailed by Pervez (2014) and as well as varieties of methods and research
techniques involving both quantitative and qualitative inquiry. They include, but are not limited to,
descriptive (Gibbs, 2008; Kruger et al., 2005; Stefanone and Gay, 2008) and inferential statistics (Meraz,
2007), content analysis of frequency measures of patterns occurrence (Chen et al., 2005), the description
of meaning (Walker, 2006), online interviews (Meho, 2006; Papacharissi, 2007), case study (Maor, 2003),
and textual analysis by hermeneutics (Lee, 1994). Researchers have also used mixed methods (Herring et
al., 2005; Papacharissi, 2007).
METHODOLOGICAL DIVERSITY
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Literature review shows an increasing use of qualitative methods for investigation of meaning, with
researchers employing ethnography (Menon, 2007), rhetorical criticism (Benson, 1996), and discourse
analysis (Curran et al., 2003). Discourse analysis’s significance in CMC research is evident in Herring’s
work (2003, 2004), where she developed techniques for discourse analysis in the specific context of
CMC, labelling it computer-mediated discourse analysis (CMDA). Among many techniques, CMDA uses
descriptive statistics on language complexity and conducts pragmatic analysis of speech acts to unravel
meaning in the text and its embedded ideological and social references.
According to Monberg (2005), ‘Over the past decade computer-mediated communication, as a
distinct intellectual focus has developed an analysis of the cultural histories, technological rhetorics, and
diverse kinds of online landscape emergent from advanced new communication technologies.’ Marra
(2006) discussed several research methods for assessing the content of CMC for online collaborative
learning. Although she found that the most common methods for evaluating the content of online fora
were frequency counts and other similar quantitative measures, she suggested, ‘In order to assess any
meaning that results from these discussions, it is necessary to perform some kind of semantic analysis of
them.’ Semantic analysis focuses on interpretation of the content (Walsham, 1995).
Interpretivist philosophy aims to provide an account of events and phenomena in terms of how
the people involved perceive and understand their own experiences instead of simply quantifying what
happens. Interpretivism is also a method for carrying out empirical studies in the light of interpretive
philosophy and theory. Lee (1991), in his discussion of the interpretive approach to organisational
research, referred to the procedures associated with ethnography, hermeneutics, phenomenology, and case
studies. Interpretivism is often contrasted with positivism. According to Lee (1991, p. 343), ‘the positivist
approach involves the manipulation of theoretical propositions using the rules of formal logic and the
rules of hypothetico-deductive logic, so that the theoretical propositions satisfy the four requirements of
falsifiability, logical consistency, relative explanatory power, and survival’ (italics in original). These
methods of investigation attempt to explain how things operate in the physical world but do not cater to
the views and opinions of the people who have a cognitive and emotional attachment to the world in
which they live. Max Weber, in introducing the concept of Verstehen (understanding), paved the way for
investigating how people interpret their worlds and their own acts. According to Natanson (1963), by
introducing the sociology of interpretation Weber argued that the primary task of the sociologists is to
understand the meaning an act has for the actor himself. Since then many different theories, approaches,
and methods for interpretive investigation have been developed; a similar proliferation is evident in CMC
research.
Pervez (2014) discussed various theoretical approaches used to address artefacts found in the
online landscape, specifically in asynchronous CMC. The literature also indicated a range of
methodological approaches undertaken in CMC research, which are presented in the following table.
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Table 1: Methods used in information science CMC research
Mode Author Methods/techniques
Email Meho (2006) Email interview
Lee (1994) Hermeneutic interpretation
Stefanone and Gay
(2008)
Descriptive statistics (frequency measure)
Kruger et al. (2005) Descriptive statistics (quantitative comparison)
Thompson (2008) Content analysis (identifying recurring patterns
and themes)
Meij (2007) Statistical measurement of attitude
Ducheneaut and
Bellotti (2003)
Contextual inquiry by interview
Tao (1996) No method of inquiry
Online collaborative
learning
Chen et al. (2005) Content analysis
Hara et al. (2000) Content analysis (Henri’s model)
Maor (2003) Case study
Chernobilsky et al.
(2005)
Mixed method (traditional and ethnographic
approach to studying activity systems)
Shih and Swan
(2005)
Questionnaire survey
Gibbs (2008) Descriptive statistics
Blog Walker (2006) Content analysis
Herring et al. (2005) Quantitative social network analysis, qualitative
analysis of blog dyads
Papacharissi (2007) Content analysis (quantification) and informal
interview
Scott (2007) Content analysis (quantification)
Meraz (2007) Descriptive and inferential statistics
Rutigliano (2007) Quantification of behaviour trends, and qualitative
analysis
Here are two mostly used methods in CMC research done by information scientists.
Content Analysis
Content analysis is a popular method in communication research that ‘analyses the denotative
order of significance. … It works through identifying and counting chosen units in a communication
system’ (Fiske, 1982). Content analysis is currently used in a number of disciplines, including marketing,
media studies, literature, rhetoric, psychology, sociology, political science, and gender studies. It is also
frequently used in information science (Julien, 2004). According to Allen (1990), ‘the term “content
analysis” denotes a family of research methods that attempts to identify and record the meaning of
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documents and other forms of communication systematically,’ it has two distinct meanings in the
information science literature. One denotes a rigorous and quantitative analysis of artefacts leading to
conclusions about their content. The other meaning of content analysis denotes the discussion of the
content of the text. Therefore, content analysis is known to have the flexibility (White and Marsh, 2006)
to support both quantitative and qualitative inquiries.
Pratt and Pratt (1995) distinguish conceptual analysis from relational analysis. Conceptual
analysis helps to establish the existence and frequency of concepts most often represented by words or
phrases in a text; and relational analysis examines the relationships among concepts in a text.
Issues of reliability and validity are crucial for content analysis. Reliability is reproducibility,
which means consistency among two or more coders working on the same data or one coder repeating his
or her work after a period of time (Tinsley and Weiss, 1975). The validity of a content analysis means
correspondence between the categories and the conclusion (Berelson, 1971). Validity is necessary to
allow the researcher to generalise from the findings.
One may use content analysis in CMC research to examine what patterns are prevalent in the
artefacts and what they might mean. The nature of the research question will determine whether to use
conceptual or relational content analysis, or a combination of the two. By doing content analysis of video
footage of bowling game, Hsieh and Chen (2005) unravelled the semantic relationships between player
information, game-related information, and video content information. This is an example of analysing
the forms of moving images using both conceptual and relational content analysis.
Hara and colleagues (2000) used Henri’s (1992) model for CMC content analysis, which deserves
special attention because ‘Henri’s model defines not only the types of skills and interactions demonstrated
in online postings, but also attempts to qualitatively define the nature and content of online interactions
that evidence cognitive development and meaningful learning. Henri’s model has provided researchers
with a structure for many ensuing qualitative analysis models’ (Marra, 2006).
Discourse Analysis
Discourse analysis also explores meanings behind text or discourse. It came to the attention of
scholars in the humanities and social sciences in the late 1960s and 1970s. Discourse analysis can be
defined as ‘the study of words and signifiers, including the form or structure of these words, the use of
language in context and the meanings or interpretation of discursive practices’ (Putnam and Fairhurst,
2001). Information science scholars began adopting this research method in the first half of the 1990s.
Frohmann (1994) identified the relevance of discourse analysis for information science while commenting
on the discipline’s user-centred focus:
5
The shift of LIS’s theoretical attention from information system to information user is especially
suited to questions of the role of LIS theories in the discursive construction of specific identities for
information, its users, and its uses. A benefit of the shift to users is that it problemetises, rather than
stabilises, the related notions of information users and information needs. When users are forced into the
centre of theoretical vision, questions arise of how their identities, and especially their needs, are
constructed in theoretical discourses. Precisely how are users of information positioned as subjects in
user-centric information theories?
Advocating discourse analysis’s importance for information science research, Frohmann (1994)
added that this method ‘permits analysis of the ways in which information, its uses, and its users are
discursively constructed, especially in theoretical discourses in Information science, such that power over
them can be exercised in specific ways.’ Budd (2006) describes two varieties of discourse analysis: one is
interpersonal – focusing on dyadic or group settings – and requires lexical comprehension as well as
commonality of belief among the communicators; this is a complex kind of discourse. The second variety
is more formal – focusing on speeches, the writing of articles and books, the publishing of results, and
artefacts and activities – and is practised in a wide range of disciplines, including information science.
Budd advocated the relevance of discourse analysis for both informal and formal information exchange
settings. Nahl (2007) employed discourse analysis to analyse text that was produced by people when
discussing their information practices. She attempted to understand people’s micro-information
behaviours. Talja (1999) used discourse analysis in unravelling information contexts.
Communicative events such as writing and conversation, all of which have forms, are examples
of discourse. Discourse analysis works with a huge range of variables, including intonation, gesture,
syntax, style, lexicon, rhetoric, meaning, speech, act, moves, strategies, turn taking, and other aspects of
interactions. It also analyses the relations between text and context, discourse and interaction, and
cognition and memory. In other words, discourse analysis serves an encompassing arena of action,
interaction, and construction of meaning. Herring’s (1994) work on identifying politeness in computer
culture by analysing texts of online chat, i.e. artefacts, is an example of how to explore value, in this case
politeness, in an online conversation. It shows the possibility of identifying forms in a discourse in order
to understand the meaning people endorse through an information environment, such as CMC, that
facilitates information dissemination and construction.
Qualitative content analysis and discourse analysis may look similar because they both tend to
reveal the meaning of text. Nevertheless, they are fundamentally different in that content analysis presents
immediate snapshot descriptions whereas discourse analysis goes beyond that. In Budd’s (2006, p. 75)
words, ‘discourse analysis addresses more than utterance. It is aimed at speech (parole), inasmuch as
speech is historically situated, occurs at a point in time, and is engaged in by numerous individuals.
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Speech, therefore, embodies epistemological, rhetorical, communicative, obfuscatory, political, cultural,
and other intentions.’
CONCLUSION
This review has explored the methods used in information science research on computer-
mediated analysis. Aligned with the findings of Pervez (2014) identifying the most frequently theoretical
perspectives such as media richness theory, hermeneutics, constructivism, activity theory, and elaboration
likelihood theory, most of the methods used in CMC research are of qualitative nature. In their preface to
the Handbook on Research on Computer Mediated Communication, Kelsey and Amant (2008) offered a
broad definition of CMC:
Computer-mediated communication can be described as any form of information humans present
or exchange by means of a computer. This information can be imparted to oneself, to another person or
group of people, or even to an imaginary audience. Likewise, CMC can be a one-to-many or one-to-one
transaction, a synchronous (real time) or asynchronous (time delayed) process and involve modes of
interaction as diverse as typed text, spoken discussions, or visual/video messages. The types of software
affecting CMC are numerous and increasing every day. Email, text messaging, video and audio players,
social networking web sites, wikis, syndicated feeds, bulletin boards, and blogs are just some of the
software enabling people to communicate.
Such a broad definition of CMC reflects and supports the extension of CMC through its adoption
of social media. This has widened CMC’s horizon, even as users find new ways to construct, disseminate,
and use information for various purposes – personal, social, and governmental. It is worth noting that the
significant number of CMC research is devoted to linguistic and discourse analysis, with attention to
semiotics. Semiotics is a proven approach for artefact analysis (Benthall, 1993; Andersen, 1990, 1991).
As the way social media is expanding, and the way people from all walks of life is sharing information of
all kind – text, image and sound – it is perhaps an indication and one can argue as a need, that more and
more investigation will adopt discourse analysis and other qualitative methods. For such methods are able
to track and understand the meaning created and shared over computer-mediated communication.
REFERENCES
Allen B, 1990. Content analysis in library and information science research. Library and Information
Science Research, Vol. 12, pp. 251–262.
Andersen PB, 1990. A Theory of Computer Semiotics: Semiotic Approaches to Construction and
Assessment of Computer Systems, Cambridge University Press, Cambridge.
Benson TW, 1996. Rhetoric, civility, and community: Political debate on computer bulletin boards.
Communication Quarterly, Vol. 44, pp. 359–378.
7
Benthall J, 1993. Disasters, Relief and the Media, Tauris, London.
Berelson B, 1971. Content Analysis in Communication Research, Hafner, New York.
Budd JM, 2006. Discourse analysis and the study of communication in LIS. Library Trends, Vol. 55, pp.
65–82.
Chen F, Lee Y, Chu HC, Wang HR and Jiang H, 2005. Effective discussions, social talks and learning: A
paradox on learning in discussion forums. In: Proceedings of the 2005 Conference on Computer
Support for Collaborative Learning: Learning 2005: The Next 10 Years!. Online
[http://portal.acm.org/citation.cfm?id=1149298 (accessed May 3, 2008)].
Chernobilsky E, Nagarajan A and Hmelo-Silver CE, 2005. Problem-based learning online: Multiple
perspectives on collaborative knowledge construction. In: Proceedings of the 2005 Conference on
Computer Support for Collaborative Learning: Learning 2005: The Next 10 Years!. Online
[http://portal.acm.org/citation.cfm?id=1149293.1149301&coll=GUIDE&dl=GUIDE&type=series&
idx=SERIES11363&part=series&WantType=Proceedings&title=CSCL (accessed May 3, 2008)].
Curran V, Kirby F, Parsons E and Lockyer J, 2003. Discourse analysis of computer-mediated
conferencing in World Wide Web-based continuing medical education. Journal of Continuing
Education in the Health Professions, Vol. 23, pp. 229–238.
Ducheneaut N and Bellotti V, 2003. Ceci n’est pas un object? Talking about things in e-mail. Human
Computer Interaction, Vol. 18, pp. 85–110.
Fiske J, 1982. Introduction to Communication Studies, Routledge, New York.
Frohmann B, 1994. Discourse analysis as a research method in library and information science. Library &
Information Science Research, Vol. 16, pp. 119–138.
Gibbs W, 2008. An analysis of temporal norms in online discussions. International Journal of Media,
Vol. 35, pp. 63–75.
Hara N, Bonk CJ, and Angeli C, 2000. Content analysis of online discussion in an applied education
psychology course. Instructional Science, Vol. 28, pp. 115–152.
Henri F, 1992. Computer conferencing and content analysis. In Kaya, A. R. (eds.) Collaborative learning
through computer conferencing, Springer, New York, pp. 117-136.
Herring SC, 1994. Politeness in computer culture: Why women thank and men flame. In: Bucholtz M,
Liang A and Hines C (eds.) Cultural Performances: Proceeding of the Third Berkely Women and
Language Conference. Berkeley, pp. 278–294.
Herring SC, 2003. Computer-mediated discourse. In: Schiffrin D, Tannen D and Hamilton HE (eds.)
Handbook of discourse analysis, Blackwell, Oxford.
Herring SC, 2004. Computer-mediated discourse analysis: An approach to researching online behavior.
In: Barab SA, Kling R and Gray JH (eds.) Designing for Virtual Communities in the Service of
Learning, Cambridge University Press, New York.
8
Herring SC, Kouper I, Paolillo JC, Scheidt LA, Tyworth M, Welsch P, Wright E and Yu N, 2005.
Conversation in the blogosphere: An analysis “From the Bottom Up” In: Proceedings of the Thirty-
Eighth Hawaii International Conference on System Sciences (HICSS-38). Online
[http://64.233.167.104/search?q=cache:9iDOIZFyg2sJ:www.blogninja.com/hicss05.blogconv.pdf+
Conversation+in+the+blogosphere:+An+analysis+%22From+the+Bottom+Up%22&hl=en&ct=cln
k&cd=1&gl=us (accessed May 4, 2008)].
Hsieh WW and Chen ALP, 2005. Constructing a bowling information system with video content analysis.
Multimedia Tools and Applications, Vol. 26, pp. 207–220.
Julien H, 2004. A content analysis of affective issues in library and information science systems work.
Information Research, Vol. 10, No. 1. Online[http://informationr.net/ir/10-1/abs6.html (accessed
January 3, 2008)].
Kelsey S and Amant KS, 2008. Preface. In: Kelsey S and Amant KS (eds.) Handbook of Research on
Computer Mediated Communication, Information Science Reference, Hershey, NY.
Kruger J, Epley N, Parker J and Ng ZW, 2005. Egocentrism over e-mail: Can we communicate as well we
think? Journal of Personality and Social Psychology, Vol. 89, pp. 925–936.
Lee AS, 1991. Integrative positivist and interpretive approaches to organizational research. Organization
Science, Vol. 2, pp. 342–365.
Lee AS, 1994. Electronic mail as a medium of rich communication: An empirical investigation using
hermeneutic interpretation. MIS Quarterly, Vol. 18, pp. 143–157.
Maor D, 2003. Teacher’s and students’ perspectives on on-line learning in a social constructivist learning
environment. Technology Pedagogy and Education, Vol. 12, pp. 201–218.
Marra R, 2006. A review of research methods for assessing content of computer-mediated discussion
forums. Journal of Interactive Learning Research, Vol. 17, pp. 243–267.
Meho LI, 2006. E-mail interviewing in qualitative research: A methodological discussion. Journal of the
American Society for Information Science and Technology, Vol. 50, pp. 1284–1295.
Meij HVD, 2007. What research has to say about gender-linked difference in CMC and does elementary
school children’s e-mail use fit this picture? Sex Roles, Vol. 57, pp. 341–354.
Menon S, 2007. A participation observation analysis of the once & again Internet message bulletin
boards. Television & New Media, Vol. 8, pp. 341–374.
Meraz S, 2007. Analyzing political conversation on the Howard Dean. In: Tremayayne M (ed.) Blogging,
Citizenship, and the Future of Media, Routledge, New York.
Monberg J, 2005. Trajectories of computer-mediated communication. Southern Communication Journal,
Vol. 70, pp. 181–186.
Nahl D, 2007. A discourse analysis technique for charting the flow of micro-information behavior.
Journal of Documentation, Vol. 63, pp. 323–339.
9
Natanson M, 1963. A study in philosophy and social sciences. In: Natanson M (ed.) Philosophy of the
Social Sciences: A reader, Random House, New York.
Papacharissi Z, 2007. Audience as media producers: Content analysis of 260 blogs In: Tremayayne M
(ed.) Blogging, Citizenship, and the Future of Media, Routledge, New York.
Pervez A, 2014. Theoretical approaches in information science research on asynchronous computer
mediated communication. International Research: Journal of Library and Information Science,
Vol. 4, pp. 427–442.
Pratt CA and Pratt CB, 1975. Comparative content analysis of food and nutrition advertisements in
Ebony, Essence, and Ladies’ Home Journal. Journal of Nutrition Education, Vol. 27, pp. 11–18.
Putnam LL and Fairhurst GT, 2001. Discourse analysis in organizations. In: Jablin FM and Putnam LL
(eds.) A New Handbook of Organizational Communication: Advances in Theory, Research, and
Methods, Sage, London.
Rutigliano L, 2007. Emergent communication networks as civic journalism. In: Tremayayne M (ed.)
Blogging, Citizenship, and the Future of Media, Routledge, New York.
Scott DT, 2007. Pundits in muckrakers’ clothing: Political blogs and the 2004 U.S. presidential election
In: Tremayayne M (ed.) Blogging, Citizenship, and the Future of Media, Routledge, New York.
Shih L and Swan K, 2005. Fostering social presence in asynchronous online class discussions. In:
Proceedings of the 2005 Conference on Computer Support for Collaborative Learning: Learning
2005: The Next 10 Years!. Online
[http://portal.acm.org/citation.cfm?id=1149293.1149372&coll=GUIDE&dl=GUIDE&type=series&
idx=SERIES11363&part=series&WantType=Proceedings&title=CSCL (accessed May 3, 2008)].
Stefanone MA and Gay G, 2008. Structural reproduction of social networks in computer-mediated
communication forums. Behaviour and Information Technology, Vol. 27, pp. 97–106.
Talja S, 1999. Analyzing qualitative interview data: The discourse analytic method. Library and
Information Science Research, Vol. 21, p. 459.
Tao L, 1996. What research reveals about email in education. In: 40th Annual Conference of the College
Reading Association, October 31–November 3, Charleston, SC, pp. 3–14.
Thompson B, 2008. Characteristics of parent–teacher e-mail communication. Communication Education,
Vol. 57, pp. 201–223.
Tinsley HE and Weiss DJ, 1975. Interrater reliability and agreement of subjective judgments. Journal of
Counseling Psychology, Vol. 22, pp. 358–376.
Walker DM, 2006. Blog commenting: A new political information space. In: Proceedings of the
American Society for Information Science and Technology, Vol. 43, No. 1. Online
[http://www3.interscience.wiley.com.oberon.ius.edu/cgi-bin/fulltext/116329038/HTMLSTART
(accessed May 21, 2008)].
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