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Computer-mediated discourse analysis: an approach to researching online communities

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Over the past fifteen years, the Internet has triggered a boom in research on human behavior. As growing numbers of people interact on a regular basis in chat rooms, web forums, listservs, email, instant messaging environments and the like, social scientists, marketers, and educators look to their behavior in an effort to understand the nature of computer-mediated communication and how it can be optimized in specific contexts of use. This effort is facilitated by the fact that people engage in socially meaningful activities online in a way that typically leaves a textual trace, making the interactions more accessible to scrutiny and reflection than is the case in ephemeral spoken communication, and enabling researchers to employ empirical, micro-level methods to shed light on macro-level phenomena. Despite this potential, much research on online behavior is anecdotal and speculative, rather than empirically grounded. Moreover, Internet research often suffers from a premature impulse to label online phenomena in broad terms, for example, all groups of people interacting online are “communities”; the language of the Internet is a single style or “genre.” Notions such as community and genre are familiar and evocative, yet notoriously slippery, and unhelpful (or worse) if applied indiscriminately. An important challenge facing Internet researchers is thus how to identify and describe online phenomena in culturally meaningful terms, while at the same time grounding their distinctions in empirically observable behavior.
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Preprint. To appear in Barab, S. A., Kling, R., & Gray, J. H. (Eds.). (2004). Designing for Virtual
Communities in the Service of Learning (pp. 338-376). New York: Cambridge University Press.
1
Computer-Mediated Discourse Analysis:
An Approach to Researching Online Behavior
Susan C. Herring
School of Library and Information Science
Indiana University
herring@indiana.edu
Introduction
Over the past fifteen years, the Internet has triggered a boom in research on human
behavior. As growing numbers of people interact on a regular basis in chat rooms, Web
forums, listservs, email, instant messaging environments and the like, social scientists,
marketers and educators look to their behavior in an effort to understand the nature of
computer-mediated communication and how it can be optimized in specific contexts of
use. This effort is facilitated by the fact that people engage in socially meaningful activities
online in a way that typically leaves a textual trace, making the interactions more
accessible to scrutiny and reflection than is the case in ephemeral spoken communication,
and enabling researchers to employ empirical, micro-level methods to shed light on macro-
level phenomena.
Despite this potential, much research on online behavior is anecdotal and
speculative, rather than empirically grounded. Moreover, Internet research often suffers
from a premature impulse to label online phenomena in broad terms, e.g., all groups of
people interacting online are "communities";1 the language of the Internet is a single style
or "genre".2 Notions such as "community" and "genre" are familiar and evocative, yet
notoriously slippery, and unhelpful (or worse) if applied indiscriminately. An important
challenge facing Internet researchers is thus how to identify and describe online
phenomena in culturally meaningful terms, while at the same time grounding their
distinctions in empirically observable behavior.
Online interaction overwhelmingly takes place by means of discourse. That is,
participants interact by means of verbal language, usually typed on a keyboard and read as
text on a computer screen. It is possible to lose sight of this fundamental fact at times,
given the complex behaviors people engage in on the Internet, from forming interpersonal
relationships (Baker, 1998) to implementing systems of group governance (Dibbell, 1993;
Kolko & Reid, 1998). Yet these behaviors are constituted through and by means of
discourse: language is doing, in the truest performative sense, on the Internet, where
physical bodies (and their actions) are technically lacking (Kolko, 1995).
Of course, many online relationships also have an offline component, and as
computer-mediated communication becomes increasingly multimodal, semiotic systems in
addition to text are becoming available for conveying meaning and "doing things" online
(cf. Austin, 1962). Nonetheless, textual communication remains an important online
activity, one that seems destined to continue for the foreseeable future. It follows that
scholars of computer-mediated behavior need methods for analyzing discourse, alongside
Computer-Mediated Discourse Analysis
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traditional social science methods such as experiments, interviews, surveys, and
ethnographic observation.
This chapter describes an approach to researching online interactive behavior
known as Computer-Mediated Discourse Analysis (CMDA). CMDA applies methods
adapted from language-focused disciplines such as linguistics, communication, and
rhetoric to the analysis of computer-mediated communication (Herring, 2001). It may be
supplemented by surveys, interviews, ethnographic observation, or other methods; it may
involve qualitative or quantitative analysis; but what defines CMDA at its core is the
analysis of logs of verbal interaction (characters, words, utterances, messages, exchanges,
threads, archives, etc.). In the broadest sense, any analysis of online behavior that is
grounded in empirical, textual observations is computer-mediated discourse analysis.3
The specific approach to computer-mediated discourse analysis described here is
informed by a linguistic perspective. That is, it views online behavior through the lens of
language, and its interpretations are grounded in observations about language and language
use. This perspective is reflected in the application of methodological paradigms that
originated in the study of spoken and written language, e.g., conversation analysis,
interactional sociolinguistics, pragmatics, text analysis, and critical discourse analysis. It
also shapes the kinds of questions that are likely to get asked. Linguists are interested in
language structure, meaning, and use, how these vary according to context, how they are
learned, and how they change over time.
CMDA can be used to study micro-level linguistic phenomena such as online
word-formation processes (Cherny, 1999), lexical choice (Ko, 1996; Yates, 1996),
sentence structure (Herring, 1998), and language switching among bilingual speakers
(Georgakopoulou, in press; Paolillo, 1996). At the same time, a language-focused approach
can be used to address macro-level phenomena such as coherence (Herring, 1999a;
Panyametheekul, 2001), community (Cherny, 1999), gender equity (Herring, 1993, 1996a,
1999b) and identity (Burkhalter, 1999), as expressed through discourse. Indeed, the
potential—and power—of CMDA is that it enables questions of broad social and
psychological significance, including notions that would otherwise be intractable to
empirical analysis, to be investigated with fine-grained empirical rigor. The present chapter
is intended as a practical contribution toward helping researchers realize this potential.
Because of its practical focus, this chapter will be most useful to readers who
already have some study of computer-mediated communication in mind and who have
given some thought to how they might approach their investigation. Readers who have
made preliminary observations about a behavior (or behaviors) of interest in a specific
online environment, and who have collected (or have access to) a relevant corpus of data,
will be even better positioned to appreciate the methodological concerns addressed here.
At the same time, the chapter is not intended as a step-by-step "how to" guide, but rather as
an overview of how a CMDA researcher might conceptualize, design and interpret a
research project involving identifying and counting discourse phenomena in a corpus of
computer-mediated text.4 For details regarding the implementation of specific analytic
methods, readers are referred to the research studies cited in the references.
I begin by providing some historical background on CMDA and the kinds of
research that have been carried out in the linguistic CMDA tradition, broadly construed. I
then present a detailed overview of one version of the CMDA approach based on the
"coding and counting" paradigm of classical content analysis, identifying a set of
Computer-Mediated Discourse Analysis
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conceptual skills necessary for carrying out a successful analysis. These skills are
illustrated with reference to the problem of analyzing ''virtual community'' in two
professional development sites on the Internet. In concluding, the limits of the coding and
counting paradigm, and the CMDA approach as a whole, are identified and future
directions are charted.
Background
The term ''computer-mediated discourse analysis'' was first coined in 1995 (see Herring,
2001), although research meeting the definitional criteria for CMDA has been carried out
since the mid-1980s (in the linguistic sense: e.g., Murray, 1985, 1988; Severinson
Eklundh, 1986), and arguably, as early as the 1970s (in the general sense: Hiltz & Turoff,
1978). Starting in the mid-1990s, and corresponding to the upsurge in computer-mediated
communication (CMC) research that followed closely on the heels of the popularization of
the Internet (Herring, 2002), an increasing number of researchers began focusing on online
discourse as a way to understand the effects of the new medium. However, different
researchers approached computer-mediated discourse with different questions, methods,
and understandings, often working in isolation from one another—and in the case of
researchers outside the United States, unaware that other researchers shared their interests.
The present chapter attempts to systematize some of the goals, understandings, and
procedures implicitly shared by this emerging cadre of researchers.
As background to the remainder of the chapter, it is useful to think of CMDA as
applying to four domains or levels of language, ranging prototypically from smallest to
largest linguistic unit of analysis: 1) structure, 2) meaning, 3) interaction, and 4) social
behavior. Structural phenomena include the use of special typography or orthography,
novel word formations, and sentence structure. At the meaning level are included the
meanings of words, utterances (e.g., speech acts) and larger functional units (e.g.,
'macrosegments', Herring, 1996b; cf. Longacre, 1992). The interactional level includes
turn-taking, topic development, and other means of negotiating interactive exchanges. The
social level includes linguistic expressions of play, conflict, power, and group membership
over multiple exchanges. In addition, participation patterns (as measured by frequency and
length of messages posted and responses received) in threads or other extended discourse
samples constitute a fifth domain of CMDA analysis.
The kinds of understandings obtainable through a language-focused approach can
be illustrated by summarizing briefly a few studies that focus on phenomena from each
domain. Non-standard spelling and typography have been analyzed structurally in Internet
Relay Chat as an example of creative play (Danet et al., 1997), on the French Minitel
system as an illustration of the tension between efficiency and expressivity (Livia, in
press), and in a social MUD as evidence of participants' "insider" status (Cherny, 1999).
Studies that consider what online participants mean by what they say—for example, by
classifying their utterances as speech acts—have discovered differences between
educational and recreational uses of IRC, as well as differences associated with
teacher/leader vs. other roles (Herring & Nix, 1997). Studies of interactional phenomena
have identified system-imposed constraints on turn-taking (Herring, 1999a;
Panyametheekul, 2001) and topic coherence (Herring & Nix, 1997; Lambiase, in press).
One stream of socially-focused CMDA, research on group identity, has identified
Computer-Mediated Discourse Analysis
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discourse styles associated with participant age (Ravert, 2001), gender (Hall, 1996;
Herring, 1993, 1996a, b, in press a), ethnicity (Paolillo, in press) and race (Burkhalter,
1999; Jacobs-Huey, in press), even in supposedly anonymous text-only CMC. Finally,
participation patterns have been observed to vary according to the synchronicity of the
medium (Condon & Cech, 2001, in press), and to reveal social influence and dominance in
online groups (Herring, in press b; Herring et al., 1992; Hert, 1997; Rafaeli & Sudweeks,
1997). This brief survey is intended to provide a sense of the range and diversity of topics
that have been researched thus far using CMDA. More detailed surveys of the findings of
previous CMDA research can be found in Herring (2001, 2002).
The CMDA Approach
CMDA is best considered an approach, rather than a "theory" or a single "method".
Although the linguistic variant described here is based on a loose set of theoretical
premises (those of linguistic discourse analysis, plus a rejection of a priori technological
determinism; see below), it is not a theory in that CMDA (as an abstract entity) makes no
predictions about the nature of computer-mediated discourse. The findings of CMDA
studies neither support nor falsify the premises of the approach, beyond confirming that it
is useful or indicating that it is in need of further refinement. Rather, the CMDA approach
allows diverse theories about discourse and computer-mediated communication to be
entertained and tested. Moreover, although its overall methodological orientation can be
characterized (see below), it is not a single method but rather a set of methods from which
the researcher selects those best suited to her data and research questions. In short, CMDA
as an approach to researching online behavior provides a methodological toolkit and a set
of theoretical lenses through which to make observations and interpret the results of
empirical analysis.
The theoretical assumptions underlying CMDA are those of linguistic discourse
analysis, broadly construed. First, it is assumed that discourse exhibits recurrent patterns.
Patterns in discourse may be produced consciously or unconsciously (Goffman, 1959); in
the latter case, a speaker is not necessarily aware of what she is doing, and thus direct
observation may produce more reliable generalizations than a self-report of her behavior.
A basic goal of discourse analysis is to identify patterns in discourse that are demonstrably
present, but that may not be immediately obvious to the casual observer or to the discourse
participants themselves. Second, it is assumed that discourse involves speaker choices.
These choices are not conditioned by purely linguistic considerations, but rather reflect
cognitive (Chafe, 1994) and social (Sacks, 1984) factors. It follows from this assumption
that discourse analysis can provide insight into non-linguistic, as well as linguistic,
phenomena. To these two assumptions about discourse, CMDA adds a third assumption
about online communication: computer-mediated discourse may be, but is not inevitably,
shaped by the technological features of computer-mediated communication systems. It is a
matter for empirical investigation in what ways, to what extent, and under what
circumstances CMC technologies shape the communication that takes place through them
(Herring, u.c.).
The basic methodological orientation of CMDA is language-focused content
analysis. This may be purely qualitative—observations of discourse phenomena in a
sample of text may be made, illustrated, and discussed—or quantitative—phenomena may
Computer-Mediated Discourse Analysis
5
be coded and counted, and summaries of their relative frequencies produced. (It should be
noted that quantitative CMDA comprises a qualitative component, e.g., in deciding what
counts as an instance of a phenomenon to be coded and counted, especially when the
phenomena of interest are semantic rather than syntactic (structural) in nature; see Bauer,
2000, and "analytical methods", below).
An example of the quantitative approach is Simeon Yates' (1996) comparison of a
corpus of asynchronous computer conferences with spoken and written English corpora
with respect to range of vocabulary, modality, and personal pronoun use. An example of
the qualitative approach is Lori Kendall's (2002) ethnographic, participant-observer study
of gendered behavior in a social MUD. An earlier ethnography of a social MUD carried
out by Lynn Cherny (1999) applies both approaches, but to different phenomena:
qualitative description of novel word creations (Ch. 3) and quantitative analysis of turn-
taking patterns (Ch. 4). Alternatively, Herring (1996b) combines the two approaches: the
same patterns of email message structure are identified by both qualitative and quantitative
means.5
As with other forms of content analysis, the CMDA researcher must meet certain
basic requirements in order to conduct a successful (i.e., valid, coherent, convincing)
analysis. She must pose a research question that is in principle answerable. She must select
methods that address the research question, and apply them to a sufficient and appropriate
corpus of data. If a "coding and counting" approach is taken, she must operationalize the
phenomena to be coded, create coding categories, and establish their reliability, e.g., by
getting multiple raters to agree on how they should be applied to a sample of the data. If
statistical methods of analysis are to be used, appropriate statistical tests must be identified
and applied. Finally, the findings must be interpreted responsibly and in relation to the
original research question. These requirements have been discussed extensively in the
literature on the conduct of empirical research (see, e.g., Alford, 1998 for research in
sociology; Bauer, 2000 for content analysis methods in communication); a basic
familiarity with them is assumed here. Of interest in the present chapter is how to apply
this general research schema to the particular constellation of issues and challenges
associated with the study of computer-mediated behavior.
As an illustration of the CMDA approach, the following sections consider a
currently popular research theme—that of "virtual community"—and how CMDA can be
applied to determine empirically whether a group of people interacting online constitutes a
community. In keeping with the focus of this volume on learning, the two online
environments chosen for illustration have professional development as their reason for
existence and both are associated with educational contexts: secondary science and
mathematics education in the first case, and tertiary linguistics education and research in
the second. To address the volume's focus on system design, the environments were
selected to contrast in their technological affordances (one is a multimodal Web site, the
other a text-based listserv); furthermore, one was intentionally designed with the goal of
creating community, whereas the other was not. A comparison of these two environments
can shed light on how the technological and social properties of CMC systems relate to the
phenomenon of virtual community.
Computer-Mediated Discourse Analysis
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Analyzing "Virtual Community"
Since it was first articulated in print (Rheingold, 1993), the concept of "virtual community"
has become increasingly fashionable in Internet research (e.g., Baym, 1995a; Cherny,
1999; Werry & Mowbray, 2001), although it has also been criticized (Fernback &
Thompson, 1995; Jones, 1995a; see also Kling & Courtright, this volume). The criticisms
include a pragmatic concern that the term has been overextended to the point of becoming
meaningless—for some writers, it seems that any online group automatically becomes a
"community"—and a philosophical skepticism that virtual community can exist at all,
given the fluid membership, reduced social accountability, and lack of shared geographical
space that characterize most groups on the Internet (e.g., McLaughlin et al., 1995). For the
purposes of the present discussion, we assume that virtual community is possible, but that
not all online groups constitute virtual communities. The task of the researcher then
becomes to determine the properties of virtual communities, and to assess the extent to
which they are (or are not) realized by specific online groups.
Two Learning Environments
Two online professional development environments will serve as examples to ground our
discussion of how CMDA can be applied to investigate virtual community. Professional
development environments are online learning environments in which people participate
voluntarily and intermittently—i.e., for the purpose of acquiring information and skills to
advance professionally—rather than in formal courses with students, instructors, and
syllabi, as is the case for distance education. In successful cases, participation in such
environments is continuous and self-sustaining, unlike course-based CMC, which is task-
focused and temporally bounded. An example of a genre of professional development
environment that dates back to the early days of computer networking is listserv discussion
groups for professionals in academic disciplines (e.g., Hert, 1997; Korenman & Wyatt,
1996). A more recent example is the growing genre of professional development Web sites
that combine discussion forums with access to documents and other online resources (e.g.,
Renninger, this volume).
The environments selected as illustrations for this chapter represent these two
types. The first, the Linguist List, was founded in November 1990 by a husband and wife
team of academic linguists as a means for disseminating information and engaging in
public discussion about issues of interest to professional (and aspiring professional)
linguists; it has been in continuous existence since 1990. Originally a text-only, by-
subscription list that made archived messages available only to subscribers, in 1994 it
established a Web site and posted the discussion archives there, making them widely
publicly accessible.6 For further description and analysis of the Linguist List, see Herring
(1992, 1996b).
The second environment, the Inquiry Learning Forum (ILF), was opened to
registered members in March 2000. It was designed with National Science Foundation
support by a team of faculty and graduate students in the School of Education at Indiana
University, with the explicit goal of fostering online community among secondary math
and science in-service and pre-service teachers interested in the inquiry learning approach
(National Research Council, 2000). Members must go to the ILF Web site to post
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messages and access the other resources there (which include videos of teachers using
inquiry methods in their classrooms); past messages remain on the site alongside current
messages. For further description and analysis of the ILF, see Barab, MaKinster, &
Scheckler (this volume) and Herring, Martinson & Scheckler (2002).
These environments are plausible candidates for virtual community status in
several respects. First, both bring together people who arguably already constitute real-
world professional communities: academic linguists and secondary math and science
educators. Second, their online participation is centered around a shared professional focus,
as in Wenger's (1998) "communities of practice." Third, the Linguist List is active and
long-lived, which some might take as prima facie evidence that it has achieved online
community status. In contrast, the ILF has struggled to establish and maintain an active
level of participation, but might be considered to have a prima facie claim to community
status on the grounds that it was explicitly designed to support community (Barab,
MaKinster, Moore, Cunningham, & The ILF Design Team, in press). For these reasons, it
is germane to ask: To what extent does participation in these two environments in fact
constitute "community" (as opposed to being simply "people interacting online")?
The following sections describe how a researcher making use of CMDA might go
about addressing this question. Five conceptual skills involved in the research process are
highlighted and discussed, first, with reference to CMDA in general, second, with
reference to virtual communities, and last, with reference to the two professional
development sites. The order of presentation of the five skills is roughly sequential (i.e., a
researcher generally starts with the first, and progresses to the last), although the research
process—in CMDA, no less than in other scientific disciplines—is frequently iterative,
involving many feedback loops (Harwood et al., 2001). However, it is important to stress
that what follows is not intended as an analysis in and of itself; to answer the question of
what constitutes online community definitively would take us well beyond the scope of the
present chapter.
Research Questions
To carry out an investigation by means of CMDA, it is first necessary to have a research
question, a problem to which the analyst desires to find a solution. Typically, the research
question is based on prior observation—the researcher may have noticed some online
behavior or behaviors and may have formed a preliminary hypothesis concerning them.
Articulating a research question is a first step towards testing the hypothesis.
A good CMDA research question has four characteristics:
1) It is empirically answerable from the available data;
2) it is non-trivial;
3) it is motivated by a hypothesis; and
4) it is open-ended.
Each of these characteristics is discussed below.
A CMDA research question should ideally ask about empirically-observable
phenomena, or phenomena that can be operationalized empirically, as opposed to purely
subjective or evaluative ones. A question about the nature and frequency of joking in an
online forum, for example, can be addressed empirically more readily than a question
Computer-Mediated Discourse Analysis
8
about whether the participants are having fun. Further, the question should be answerable
from the data selected for analysis. For example, if only computer-mediated data are to be
examined, the question should not ask whether CMC is better or worse than face-to-face
communication along some dimension of comparison, since the CMC data can not tell us
anything directly about face-to-face communication. Equally important in CMDA, the
question should be answerable on the basis of textual evidence. Text is direct evidence of
behavior, but it can only be indirect evidence of what people know, feel, or think. If it is
important that the researcher try to understand participants' internal conscious or
unconscious states, CMDA should be supplemented with other methods of analysis such as
interviews or psychological experiments.
A good research question should be non-trivial; that is, the answer should be of
some ostensible interest to at least a portion of the larger research community, and not
already known in advance. Additionally, the research question should not be worded so as
to presuppose an answer; that is, the answer should not appear to be a foregone conclusion.
At the same time, a research question motivated by a hypothesis—even if it is no
more than an informal hunch—is more interesting and more interpretable than one that is
not. Note that it is not necessary to posit a hypothesis that the researcher expects will be
confirmed by the results of the analysis, although the hypothesis should be prima facie
plausible. In some cases, a researcher may advance a popular hypothesis that she suspects
is incorrect, in order to disprove it. For example, she might postulate that participant
gender is invisible in CMC (a commonly held view in the early 1990s, based on the
paucity of social status cues in text-only CMC), suspecting that such is not the case in her
data.7 The empirical results, if negative, are all the more illuminating for running counter to
the prevailing wisdom.
Ideally, whether the researcher's hypothesis is supported or not, the results of the
study should contribute new knowledge. Phrasing the question as an open-ended question
(what, why, when, where, who, how) leaves the door open to unexpected findings to a
greater extent than closed (yes/no) questions, generally speaking. One caveat is that
unexpected answers to yes/no questions can be informative, as noted above, when the
hypothesis underlying the question is favored by popular opinion or common sense, but
receives no empirical support. Similarly, positive support for an unobvious hypothesis can
also cause us to understand the world in new ways. However, support for obvious
hypotheses does not advance knowledge, nor does lack of support for unobvious
hypotheses. In contrast, a systematic study will always reveal something new in response
to a well-crafted "what", "why", or "how" question.
What kinds of questions about virtual community can be researched from a CMDA
perspective? Although all are legitimate foci of intellectual curiosity, the researcher is
setting herself up for difficulty if she asks questions such as: i) "Does virtual community
exist?" ii) "Is virtual community a good thing?" iii) "Does membership in virtual
communities satisfy needs previously satisfied only in face-to-face communities?" or iv)
"Do people interact regularly in groups online?" Note, first of all, that these are closed
questions, to which the answer can only be "yes" or "no". In addition, the first is effectively
biased towards an affirmative answer, in that exhaustive evidence would be required in
order to answer it negatively. The second question both presupposes the existence of
virtual community (a problem if virtual community hasn't already been empirically
demonstrated) and asks a subjective, evaluative question about it; "goodness" is difficult to
Computer-Mediated Discourse Analysis
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measure empirically. The third question involves a comparison; it can only be answered if
empirical evidence (gathered by comparable means) is available from both "virtual
communities" (presupposed to exist) and face-to-face communities. Finally, the fourth
question, although neutrally worded and answerable, is trivial—the answer is obvious to
anyone who has spent any time on the Internet.
The following, in contrast, are examples of open-ended questions that can usefully
be addressed using CMDA: a) "What are the discourse characteristics of a virtual
community?" (b) "What causes an online group to become a community?" c) "What causes
a virtual community to die?" d) "How do virtual communities differ from face-to-face
communities?"8 e) "What happens to face-to-face communities when they go online?" and
f) "In what ways do communities constituted exclusively online differ from online
communities that also meet face-to-face?" However, these questions are not all equally
easy to answer; their answerability depends on the data available for investigation. Thus,
for example, a)-d) and f) require an independent determination of virtual community, e.g.,
in terms of participants' perceptions; b), c), and e) require longitudinal data; and d) and e)
require face-to-face data (see discussion of "data" below).
In addition, particular data samples will generally exhibit characteristics that invite
more specific questions to be asked about them. The question raised in the previous
section—"[t]o what extent does participation in these two environments constitute
'community' (as opposed to being simply 'people interacting online')?"—is a
straightforward application of question (a) to the Linguist List and the ILF data samples.
But these samples, by their nature, also give rise to questions about virtual community and
professional development (e.g., "What is the nature of virtual community in professional
development environments, and how does it differ from virtual community in structured
learning environments / unstructured social environments / etc.?"). Furthermore, the two
environments contrast according to a number of technological and social dimensions, as
summarized in Table 1.9 Additional questions can be asked to focus on the contributing
effects of a particular dimension to online behavior (e.g., "Is a multimodal environment
more conducive to virtual community than a text-only environment?"; or "How does the
self-presentation of the group 'owners' (e.g., as peers or as experts) affect the likelihood
that a group will develop community characteristics?").
The comparison of the two groups in Table 1 suggests too many possible questions
about the variables that condition virtual community, in fact. Ideally, two data samples that
are compared should differ according to only one dimension, such that if differences in
behavior are found between the samples, they can plausibly be attributed to that dimension
of variation. If, however, it turns out that either the Linguist List or the ILF exhibits more
"community" behaviors than the other, to what should the difference be attributed:
(multi)modality? ease of posting messages? ease of access to the group's history?
availability of face-to-face interaction? the intentions/behavior of the group's founders? etc.
Causal indeterminacy is a common problem in research that analyzes naturally occurring
behavior.10 The experimental research paradigm controls for this by holding all variables
constant except for the variable that is hypothesized to condition the experimental result.
For examples of experimental research that make use of CMDA methods, see Condon &
Cech (1996a, 1996b, 2001).
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Table 1. Dimensions of contrast between the Linguist List and the ILF
Linguist List
ILF
Text-only
Multimodal
(text + video + limited audio and
graphics)
Messages come to subscriber
("push" technology)11
Member must go to site to post
messages ("pull" technology)
Archives stored separately
Past messages appear alongside current
ones
Public (by subscription)
Semi-public (by registration; password
required; limited membership)
Pre-existing face-to-face "community"
(meets at annual professional meeting)
Loosely defined pre-existing
"community" (most members have
never met face-to-face)
Relatively homogeneous population of
users (academic linguists at
universities) with similar access
opportunities
Heterogeneous population of users (pre-
service teachers; in-service teachers;
ILF researchers) with differential access
Founders' goals were specific, limited
in scope (i.e., information exchange &
discussion)
Creators' goals were broad, ambitious
(i.e., create intentional community;
foster inquiry learning)
Moderators present themselves as
peers, "facilitators" (but exercise
behind-the-scene control over postings)
ILF development team members have
higher status (but post messages
themselves, and do not control
postings)
Discussion is on topics selected by
participants
Discussion is often focused around
artifacts (video clips; instructional
technology; lesson plans, etc.)
Data Selection
In CMDA, as in other empirical social science approaches, a data sample must be selected
that is appropriate to the study. By "appropriate" is meant that the sample should be of a
nature and size to answer the research question(s); if the research question involves a
comparison, more than one sample may be required. Each of these considerations is
discussed below. For the purposes of this discussion, it is assumed that the data of interest
are produced naturally (i.e., by online discourse participants for their own purposes), and
logged or culled from online archives by the researcher, rather than elicited experimentally.
Computer-Mediated Discourse Analysis
11
It is often impossible to examine all the phenomena of relevance to a particular
research question; this is especially true in CMDA, for which a vast amount of textual data
is available in the form of online interactions. (Even in groups with relatively low
participation, such as the ILF in its first year, the total amount of text quickly adds up to
more than can easily be analyzed by a human coder using micro-linguistic methods.) For
this reason, the researcher must usually select a sample from the totality of the available
data. In CMDA, this is rarely done randomly, since random sampling sacrifices context,
and context is important in interpreting discourse analysis results. Rather, data samples
tend to be motivated (e.g., selected according to theme, time, phenomenon, individual or
group), or samples of convenience (i.e., what the researcher happens to have access to at
the time). Some advantages and disadvantages of these various sampling techniques are
summarized in Table 2.
Table 2. CMDA data sampling techniques
Advantages
Disadvantages
Random
(e.g., each message selected
or not by a coin toss)
representativeness;
generalizability
loss of context &
coherence; requires
complete data set to draw
from
By theme
(e.g., all messages in a
particular thread)
topical coherence; a data set
free of extraneous messages
excludes other activities
that occur at the same time
By time
(e.g., all messages in a
particular day/week/month)
rich in context; necessary
for longitudinal analysis
may truncate interactions,
and/or result in very large
samples
By phenomenon
(e.g., only instances of
joking; conflict negotiation)
enables in-depth analysis of
the phenomenon (useful
when phenomenon is rare)
loss of context; no
conclusions possible re:
distribution
By individual or group
(all messages posted by an
individual or members of a
demographic group, e.g.,
women, students)
enables focus on individual
or group (useful for
comparing across
individuals or groups)
loss of context (especially
temporal sequence
relations); no conclusions
possible re: interaction
Convenience
(whatever data are available
to hand)
convenience
unsystematic; sample may
not be best suited to the
purposes of the study
Of the techniques in Table 2, temporal sampling preserves the richest context. If a
long enough continuous time period is captured, the sample will most likely include
coherent threads, thereby incorporating the advantages of thematic sampling as well.
Analogously, a thematic sample is typically organized by time, enabling some longitudinal
Computer-Mediated Discourse Analysis
12
observations to be made. Because of their multiple advantages, these two sample types are
favored in CMDA research. In addition, it is possible to break a sample of any type down
by individual or group, thereby achieving additional focus while avoiding the
disadvantages of individual or group sampling. (For example, an extended thread was
isolated for analysis from the Linguist List, then broken down by gender of participants, in
Herring, 1992, 1996b).
The richest possible context is required for the purposes of analyzing virtual
community, as are data that can show change over time, if questions about the inception,
evolution, and demise of virtual communities are to be addressed. The sample should
include, as much as is possible, the typical activities carried out on the site. These
considerations suggest intermittent time-based sampling (e.g., several weeks at a time at
intervals throughout a year) as particularly appropriate.12 Ideally, in any analysis of virtual
community, textual analysis would be supplemented by ongoing participant observation.13
The ILF environment imposes some limitations on sampling, as well as suggesting
alternative sampling possibilities. Discussions take place in different parts of the ILF site,
making it difficult to capture a representative overall time-based sample; rather, samples
must be collected from individual "rooms" and collated, if a single sample is required.
Moreover, discussions in the "classroom" portion of the ILF site are organized around
videos of teachers using inquiry methods in their classrooms, with one discussion forum
attached to each video (Herring et al., 2002). This configuration suggests new categories of
data sampling: by room, and by artifact (in this case, video). A sampling technique based
on units of interaction determined by the site design (and/or by participants' actual usage)
has the advantage of allowing discourse patterns to emerge that are internally coherent to
such units, whereas if data are combined across units, those patterns might be less
apparent.
How much data is required to conduct a successful CMDA study? There is no
simple answer to this question. The data should be sufficient to address the research
question, such that tests of statistical significance could meaningfully be conducted on the
key findings (regardless of whether or not the researcher actually conducts such tests).
What counts as a sufficient amount of data will depend, therefore, on the frequency of
occurrence of the analytical phenomenon in the data sample, the number of coding
categories employed to describe the phenomenon, and the number of external factors that
are allowed to vary (e.g., modality; topic of discussion; participant gender). Two general
rules of thumb are 1) the more infrequent the phenomenon in the data, the larger the
sample should be, and 2) the more variables considered in the analysis, the larger the
sample should be. This is so that 1) enough instances of the phenomenon are available to
analyze, and 2) when the sample is broken down into sub-samples for purposes of
comparison, there are still enough instances in each category to allow for statistical
testing.14 Since it is often difficult to know all of this in advance, a recommended practice
is to start with a pilot study based on a small amount of data, and expand the sample size as
necessary in a larger study, according to the tendencies revealed in the pilot study.
A related issue concerns the number of samples required for purposes of
comparative analysis. Above we noted that some CMDA research questions presuppose a
comparison with face-to-face discourse. While it may be legitimate to draw a comparison
with previous research on face-to-face communication in interpreting one's results (see
"interpretation" below), no key results should be founded on such a comparison, unless the
Computer-Mediated Discourse Analysis
13
researcher can assure that the face-to-face study was carried out using comparable methods
(e.g., because it was conducted by the researcher herself, or because the same methods that
were applied in the face-to-face study were applied to the computer-mediated data).
Otherwise, a comparable face-to-face sample is normally required. What the researcher
hopes to find are cases in which the same people are communicating about the same topics,
for the same purposes, both face-to-face and via CMC. Unfortunately, this situation rarely
occurs naturally. Left to their own devices, people tend to use different modalities for
different communicative purposes; moreover, CMC enables certain behaviors that would
be difficult or impossible offline,15 and vice versa. Data collected in experimental settings
are superior to naturally-occurring data for the purposes of comparing CMC with face-to-
face (and traditional written) communication (see, e.g., Condon & Cech, 1996a, 1996b,
2001). However, since evidence of community is highly unlikely to surface in laboratory
settings, given that experimental subjects typically have no past (or anticipated future)
interaction (Walther, 1996), empirical comparison of face-to-face and online community is
difficult. This may be one question for which interpretive, rather than strictly empirical,
answers will have to suffice for the present time (cf. Etzioni, 1999).
Multiple CMC samples (or sub-samples) may also be required in order to carry out
a single study, depending on the research question. These are usually easier to collect, but
care should be taken to hold constant as many dimensions of variation as possible, to
maximize the interpretability of the results. Our two professional development samples in
fact vary according to too many dimensions to enable straightforward comparison, as noted
above. A better example of contrasting samples is Paolillo's (in press) comparison of a(n
asynchronous) Usenet newsgroup and a (synchronous) IRC channel frequented by the
same participant demographic group (and to some extent, the same individuals): expatriate
South Asians. When differences are found in language choice in the two samples, they can
plausibly be attributed to differences in synchronicity between the two CMC modes.
Dividing a larger sample into sub-samples by demographic group, topic, or other
category is another means to insure that the sub-samples share all but one feature.
Applying this principle to research on virtual community, we might, for example, compare
the behaviors of individuals within a single group who are known to interact face-to-face
with other group members, with those individuals who do not, to test the hypothesis that
face-to-face contact enhances involvement in online community (cf. Diani, 2000). Or we
might consider participant behavior by role or status in relation to hypothesized
community behaviors. In the case of the Linguist List, the behavior of professors might be
compared with that of students, or U.S. linguists with non-U.S. linguists; in the ILF, pre-
service teachers might be compared with in-service teachers, and teachers with researchers,
to determine if higher status groups are more invested in the "community" than lower
status groups.16
Operationalization of Key Concepts
The coding and counting approach to CMDA research described in this chapter requires
that key concepts be operationalizable (and operationalized) in empirically measurable
terms. This entails defining the concepts unambiguously, such that another researcher,
examining the same data, could in principle reproduce the identification of a given token as
an exemplar of the concept.17 Equally or more important, it is necessary to define a concept
Computer-Mediated Discourse Analysis
14
in concrete, textual terms in order to be able to code it consistently. In the case of highly
abstract concepts, this necessarily entails a reduction (and a risk of distortion) of the
concept; content analysis is sometimes criticized on these grounds (cf. Bauer, 2000). At the
same time, it is the requirement of operationalization, more than any other single
requirement, that lends CMDA its rigor and makes it a useful tool for getting an empirical
grasp on otherwise slippery or intractable concepts.
Concepts vary in the degree to which they are inherently operationalizable. This
can be represented as a continuum, as in Figure 1. In a previous section, it was suggested
that a researcher should avoid asking questions about concepts that are too far towards the
subjective, abstract end of the continuum. In fact, such questions are often the most
interesting to ask, but in order to address them quantitatively using CMDA, they must be
defined in terms of textual phenomena that can be directly observed, coded, and counted.
Thus, for example, concepts of widespread interest in CMC research such as affect,
democracy, depth (of discussion), empowerment learning, trust, etc. can be operationalized
by identifying discourse behaviors (plausibly) characteristic of each phenomenon and then
articulating interpretive links between those behaviors and the larger concepts. (We will
see how this might be done for the concept of virtual community below.) Alternatively, it
might be necessary to supplement CMDA with other methods in order to make a
meaningful demonstration that the evidence addresses the concept. For example, it is
unlikely that CMC evidence alone could make a definitive case for changes in offline
states of affairs; such a demonstration would normally require offline evidence,
observational or self-reported.
Figure 1. Continuum of operationalizability
More operationalizable Less operationalizable
<------------------------------------------------------------------------------------------------------>
external, directly observable behavior internal, subjective states
concrete, bounded, measurable abstract, ambiguous, generalized
directly related to coding categories not obviously related to coding categories
"Community" is an inherently abstract concept. It also has a subjective component,
especially when it is applied to online contexts, where it is always, in some sense, a
metaphorical extension of the literal meaning of community as "grounded in a shared
physical space" (cf. Jones, 1995a). Accordingly, definitions of community (and virtual
community) abound, although Wellman's (2001) tripartite characterization of community
as providing "sociability, support, and identity" constitutes a useful point of departure.
More specifically, six sets of criteria can be identified from the literature on virtual
community (e.g., Haythornthwaite et al., 2000; Jones, 1995a, 1995b; Reid, 1991, 1994,
1998; Riel, this volume):
1) active, self-sustaining participation; a core of regular participants
2) shared history, purpose, culture, norms and values
3) solidarity, support, reciprocity
4) criticism, conflict, means of conflict resolution
5) self-awareness of group as an entity distinct from other groups
6) emergence of roles, hierarchy, governance, rituals
Computer-Mediated Discourse Analysis
15
Criteria 1) and 4) relate to "sociability"; criteria 3) and 6) (loosely) to "support", and
criteria 2) and 5) to "identity."18
These six criteria suggest concrete ways in which the notion of "virtual
community" might be broken down into component behaviors that can be objectively
assessed.
1) Participation can be measured over time, and core participants identified on the
basis of frequency of posting and rate of response received to messages posted (Herring, in
press b), or via text-based social network analysis (Paolillo, 2001; cf. Koku & Wellman,
this volume).
2) Shared history can be assessed through the availability and use of archives
(Millen, 2000). Culture is indexed through the use of group-specific abbreviations, jargon,
and language routines (Baym, 1995a; Cherny, 1999; Jacobs-Huey, in press; Kendall,
1996), as well as through choice of language, register, and dialect (Georgakopoulou, in
press; Paolillo, 1996). Norms and values are revealed through an examination of netiquette
statements (Herring, 1996a), FAQs (Voth, 1999) and verbal reactions to violations of
appropriate conduct (McLaughlin et al., 1995; Weber, in press).
3) Solidarity can be measured through the use of verbal humor (Baym, 1995b);
support through speech act analysis focusing, e.g., on acts of positive politeness (Herring,
1994); and reciprocity through analysis of turn initiation and response (Rafaeli &
Sudweeks, 1997).
4) Criticism and conflict can be analyzed through speech acts violating positive
politeness (Herring, 1994). Conflict resolution might usefully be considered as an
interactive sequence of acts (cf. Condon & Cech, 1996b on decision-making sequences); it
also lends itself to ethnographic analysis (e.g., Cherny, 1999).
5) A group's self-awareness can be manifested in its members' references to the
group as a group, and in 'us vs. them" language, particularly in statements to the effect,
"We do things this way here" (implying an awareness that they might be done differently
elsewhere; Weber, in press). (See also "norms" above.)
6) Evidence of roles and hierarchy can be adduced through participation patterns
(see "participation" above) and speech act analysis (e.g., Herring & Nix, 1997, which
considers the different acts performed by group leaders and non-leaders). The study of
governance and ritual would appear to require an ethnographic approach in which a
group's practices are observed over time and described in terms of their meanings to
participants (Cherny, 1999; Jacobson, 1996; Kolko & Reid, 1998). Note, however, that the
reification of cultural practices in the form of governance and ritual appears to represent a
relatively advanced stage of community (see, e.g., Dibbell's 1993 account of how this
happened in LambdaMOO); thus it probably should not be taken as part of the basic
definition of virtual community.
Some of the above features are more useful than others as potential indicators of
virtual community on the Linguist List and the ILF. Certain features occur rarely or not at
all in either group: language routines, code switching, humor, and governance and ritual.
Their relative absence is due to a variety of circumstances, for example the professional
(serious) focus of the groups, and the fact that their members are proficient in written
English.19 Other features occur only or nearly exclusively on the Linguist List, e.g.,
criticism, conflict, and netiquette statements.20 Conversely, such features as participation
patterns, reciprocity, indicators of group self-awareness, and evidence of roles and
Computer-Mediated Discourse Analysis
16
hierarchy are evident in both and might usefully be assessed as community indicators for
these environments.
Analytical Methods
Analytical methods in CMDA are drawn from discourse analysis and other language-
related paradigms, adapted to address the properties of computer-mediated communication.
In principle, nearly any language-related method could be so adapted; in practice, this
chapter focuses on methods of linguistic discourse analysis, these being the methods with
which the author is most familiar. These include approaches traditionally used to analyze
written text and spoken conversation, approaches to discourse as social interaction, and
critical (socio-political) approaches.
Given that we have already identified content analysis as the basic methodological
apparatus of CMDA, the question might arise as to what the more specific linguistic
approaches add to the research endeavor. In fact, it is possible to conduct a perfectly
responsible CMDA analysis without drawing on any more specific paradigm than
language-focused content analysis. For example, one could let the phenomenon of interest
emerge out of a sample of computer-mediated data and devise coding categories on the
basis of the observed phenomenon, as in the grounded theory approach (Glaser & Strauss,
1967). This approach is especially well suited to analyzing new and as yet relatively
undescribed forms of CMC, in that it allows the researcher to remain open to the
possibility of discovering novel phenomena, rather than making the assumption in advance
that certain categories of phenomena will be found.
However, grounded theory is less useful for evaluating specific research
hypotheses, or for making systematic comparisons across data samples. For these
purposes, the CMDA researcher can profit from the structure, experience, and
understandings available through specific discourse analysis paradigms. Such paradigms
define issues of theoretical interest, a set of discourse phenomena about which much may
already be known in other modalities and contexts, and discovery procedures for revealing
the patterns and constraints that characterize the phenomena. Table 3 summarizes this
information for five discourse analysis paradigms commonly invoked in CMDA research.
However, while it is useful to be cognizant of these research paradigms as part of
the CMDA toolkit, and to draw on them as appropriate, most CMDA research does not
take as its point of departure a paradigm, but rather observations about online behavior as
manifested through discourse. That is, rather than starting off with the intention of using
conversation analysis (for example) to investigate some aspect of CMC and then selecting
a behavior to focus on, a researcher is more likely to become interested in studying patterns
of message exchange (for example), and then select conversation analysis as a useful
methodological tool. In this sense, the approach is inductive—the phenomena of interest
are primary—rather than deductive, or theory-driven. This orientation is reflected in Table
4, in which essentially the same CMDA issues and methods are re-organized around the
four domains of language (plus participation) identified at the beginning of this chapter.
Each domain includes sub-sets of linguistic phenomena, listed in the second column of
Table 4.
Computer-Mediated Discourse Analysis
17
Table 3. Five discourse analysis paradigms
Issues
Phenomena
Procedures
Text Analysis
(cf. Longacre, 1996)
classification,
description,
"texture" of texts
genres, schematic
organization,
reference, salience,
cohesion, etc.
identification of
structural
regularities within
and across texts
Conversation
Analysis
(cf. Psathas, 1995)
interaction as a
jointly negotiated
accomplishment
turn-taking,
sequences, topic
development, etc.
close analysis of the
mechanics of
interaction; unit is
the turn
Pragmatics
(cf. Levinson, 1983)
language as an
activity—"doing
things" with words
speech acts,
relevance, politeness,
etc.
interpretation of
speakers' intentions
from discourse
evidence
Interactional
Sociolinguistics
(cf. Gumperz, 1982;
Tannen, 1993)
role of culture in
shaping and
interpreting
interaction
verbal genres,
discourse styles,
(mis)communication,
framing, etc.
analysis of the
socio-cultural
meanings indexed
through interaction
Critical Discourse
Analysis
(cf. Fairclough, 1992)
discourse as a site in
which power and
meaning are
contested and
negotiated
transitivity,
presupposition,
intertextuality,
conversational
control, etc.
interpretation of
meaning and
structure in relation
to ideology, power
dynamics
Computer-Mediated Discourse Analysis
18
Table 4. Four domains of language
Phenomena
Issues
Methods
typography,
orthography,
morphology, syntax,
discourse schemata
genre characteristics,
orality, efficiency,
expressivity,
complexity
Structural/Descriptive
Linguistics, Text
Analysis
meaning of words,
utterances (speech
acts), macrosegments
what the speaker
intends, what is
accomplished through
language
Semantics,
Pragmatics
turns, sequences,
exchanges, threads
interactivity, timing,
coherence, interaction
as co-constructed,
topic development
Conversation
Analysis,
Ethnomethodology
linguistic expressions
of status, conflict,
negotiation, face-
management, play;
discourse styles, etc.
social dynamics,
power, influence,
identity
Interactional
Sociolinguistics,
Critical Discourse
Analysis
Participation, while not a level of linguistic analysis per se, constitutes a fifth domain, in
which the phenomena of interest are number of messages and responses and message and
thread length. Such numbers can be interpreted to address social issues such as power,
influence, engagement, roles, and hierarchy. Participation is not associated with a
particular set of discourse analysis methods, but rather with descriptive statistics (i.e., the
phenomena are simply counted).
Bauer (2000) draws a useful distinction in content analysis between "syntactic"
(structural) and "semantic" phenomena. The former are invariant in form, or their members
comprise a limited set of variants that can be formally identified. Examples of structural
CMC phenomena include emoticons, abbreviations, lexical items (such as personal
pronouns), word formatives (such as cyber-), syntactic patterns (such as passive voice),
and quoting (when marked by a formal signal, such as quotation marks or an angle bracket
> at the beginning of a line of text). Such phenomena are objectively identifiable; they can
be coded and counted more or less automatically, on the basis of a predefined set of
structural features. Obviously, these are advantages if the researcher wishes to conduct
computer-assisted data analysis.
Semantic coding categories, in contrast, hold the meaning or function constant, but
vary (sometimes endlessly) in form. Examples of semantic CMC phenomena include
speech acts and most social phenomena such as conflict and politeness.21 Coding such
phenomena necessarily involves an interpretive, subjective component; in most cases it can
only be carried out by human coders. Despite the greater challenges they pose for
empirical investigation, semantic phenomena are often the most interesting to study.
Computer-Mediated Discourse Analysis
19
Empirical rigor can be maintained if the researcher operationalizes and defines each coding
category in explicit terms and applies the codes consistently to the data. To insure
consistency of coding, inter-rater reliability measurements can be taken in CMDA, as in
other forms of content analysis. This is especially advisable when the coding incorporates
a subjective component.
The structural language phenomena in Table 4 are generally "structural" (or
"syntactic") in Bauer's sense. Interactional phenomena such as threading (based on subject
line) can also be identified on structural grounds. To the extent that key words identify
social phenomena, the frequency of those words can be counted, making structural
methods appropriate to some social questions as well. Word and message counts are purely
structural. In contrast, meaning, most social phenomena, and any interactional phenomena
that require interpretation are "semantic" in Bauer's sense. One practical consequence of
the greater ease with which structural phenomena can be automated is that analyses of such
phenomena can be carried out on large samples of data. Conversely, semantic analyses,
because they must be done "by hand," effectively limit the amount of data that can be
analyzed.22
In the discussion of "operationalization" above, various discourse behaviors were
identified as possible indicators of virtual community. These represent both structural and
semantic phenomena, and span all five domains of CMDA. Table 5 summarizes these
behaviors.
Table 5. Discourse behaviors hypothesized to indicate virtual community
structure
jargon, references to group, in-group/out-
group language
meaning
exchange of knowledge, negotiation of
meaning (speech acts)
interaction
reciprocity, extended (in-depth) threads,
core participants
social behavior
solidarity, conflict management, norms of
appropriateness
participation
frequent, regular, self-sustaining activity
over time
In an actual CMDA analysis of the evidence for virtual community in the Linguist
List and the ILF, one or more behaviors would be selected from Table 5 and explicit
coding categories devised for each. For example, in-group/out-group language might be
operationalized structurally as the uses of first-person plural pronouns ("we", "us", etc.) in
contrast to third-person plural pronouns ("they", "them", etc.); reciprocity might be
operationalized interactionally as "response to previous message" or "response to previous
message exchange" (cf. Rafaeli & Sudweeks, 1997); and solidarity might be
operationalized in social terms as the occurrence of humorous utterances (which would, in
turn, need to be explicitly defined). An investigation that attempted to address all of the
Computer-Mediated Discourse Analysis
20
behaviors in Table 5 would probably not be feasible, since each behavior would need to be
coded whenever it applies in a sufficiently large enough sample to achieve meaningful
results for each demographic, temporal, topical, or other sub-division of the data that is
being considered, for each of the two groups. Unless many of the features were coded
automatically, the coding involved would be excessively time-consuming, and the results
too numerous to present and discuss in an article-length work (although such a project
might be appropriate in scope for a doctoral dissertation). In light of these constraints—and
since in any event few studies are able to analyze all the possible evidence pertinent to a
given research question—a researcher will normally select those features to code that she
believes will produce the most valid and convincing results in relation to the research
question, which in this case concerns the presence or absence of virtual community.
Although space and scope considerations prevent us from undertaking a full-
fledged analysis of the hypothesized community behaviors in the Linguist List and the ILF
in this chapter, a superficial consideration of the behaviors in Table 5 nonetheless reveals
some differences between the two groups. The Linguist List has an explicit set of norms
and guidelines for appropriate posting behaviors that are periodically posted to the list;
such norms, if they exist on the ILF at all, are implicit. The Linguist List is characterized
by regular conflict episodes, some of which are resolved behind the scenes by the
moderators (see, e.g., Herring et al., 1995). Indeed, conflict was a feature of the Linguist
List from the outset (Herring, 1992). In contrast, the ILF has virtually no conflict episodes.
Perhaps most significantly, the Linguist List is active and self-sustaining; it grew rapidly
from about 500 to 4000 subscribers in the first year, doubling to 8000 after a few years;
today, at over 12,000 subscribers, message volume is so great as to overwhelm some
subscribers, even when messages are consolidated and distributed as daily digests. In
contrast, the ILF has had to work hard to recruit members—as of January 2002, the
number was around 1000, most of them pre-service teachers who were required to
subscribe as part of their course work at Indiana University—and most members do not
post. If they do, they do not return to the site subsequently, and few exchanges turn into
extended threads.
There are also similarities. Both sites make use of professional jargon; both
reference themselves as an in-group in relation to an out-group (non-linguists; students);
both exchange knowledge23 (although more of this takes place on the Linguist List than on
the ILF); and both make limited use of expressions of solidarity. In a quantitative study,
these observations would be supported with numerical evidence of frequency distributions
for each behavior, compared across the two sites. How might we interpret such evidence in
relation to the question of whether the two environments are virtual communities?
Issues of Interpretation
Responsible interpretation of research findings is necessary to insure the validity of any
study. Skillful interpretation, moreover, makes the difference between a competent
investigation and an insightful one. Interpretation is thus both a craft and an art.
Interpretation of the results of CMDA should ideally take into account medium and
situational variables, and take place on three levels: close to the data, close to the research
question, and (optionally) beyond the research question.
Computer-Mediated Discourse Analysis
21
Medium and situational variables are dimensions according to which computer-
mediated data can vary and which potentially condition significant variation in online
behavior. An example of a medium variable is synchronicity; an example of a situational
variable is participant demographics (for a longer list of variables of each type, see
Herring, u.c.). Such variables often enter into decisions about data selection early in the
research process and can function as explicit dimensions of contrast within a study—for
example, a synchronous sample may be compared with an asynchronous sample; native
English speakers may be compared with non-native English speakers, males with females,
teachers with students, and so forth. These same dimensions are also relevant in
interpreting analytical results, even in studies with relatively homogeneous data sets.
The issue is one of generalizability of the research findings: for what kinds of
CMC—beyond the specific sample(s) analyzed—might the findings hold true? Strictly
speaking, every sample is unique, and thus all generalization should be undertaken with
caution. At the same time, results that do not generalize beyond the sample in the study are
less valuable and interesting than those that do, a consideration that argues against
excessive conservatism in interpretation. Advancing explanations that take into account
medium and situational variables is one way to balance these competing requirements.
Another strategy for balancing caution with generalization is to interpret the
research findings at multiple levels. Interpretation close to the data involves summarizing
and synthesizing the results obtained by applying the analytical methods to the data. At this
most conservative level of interpretation, patterns of results should be identified.
Interpretation close to the research question requires the researcher to revisit the research
questions raised at the outset of the study and indicate explicitly how the results answer the
questions. Some creative reasoning may be required here; for example, the steps necessary
to reason from the larger concepts in the research question to the specific, operationalizable
features of the text may need to be reversed. At this level of interpretation, the researcher
should also point out which results are expected and which are unexpected, and propose
explanations for the unexpected results. The third and broadest level of interpretation calls
upon the researcher to extrapolate from the findings of the study to their theoretical,
methodological, and/or practical (e.g., design) implications. This level is necessarily the
most speculative, and is not strictly speaking required to complete a study. However,
broader interpretation helps others to appreciate the significance of the analysis, and can
suggest productive avenues for further research.
Because interpretation is a creative intellectual act and because there can be more
than one possible (broad) interpretation for any given analytical result, care should be
taken that plausibility is always preserved (i.e., that the interpretations do not run counter
to the evidence, writ large). The limitations of textual evidence should also be borne in
mind: Text can only tell us what people do (and not what they really think or feel). Any
interpretations of the latter based on the former necessarily contain an element of
speculation and risk being incorrect. At the same time, the researcher should try to
construct the strongest possible evidential case for those interpretations she believes to be
true.
What can be concluded about virtual community on the basis of the discourse
evidence identified in the preceding sections? Specifically, what does CMDA reveal about
the status of the two professional development sites as virtual communities? Our
necessarily superficial analysis suggests some tentative interpretations. A close-to-the-data
Computer-Mediated Discourse Analysis
22
interpretation would summarize the results given in the last paragraph of the preceding
section: statements about the relative presence or absence of each of the community
features analyzed in each of the two professional development environments. At this level,
we might conclude that both environments manifest at least some of the hypothesized
community behaviors. At the same time, differences exist in the degree to which each
environment manifests the behaviors, and in which behaviors are manifested.
Our overall research question was: To what extent are these environments "virtual
communities"? If "community" is operationalized according to the discourse behaviors in
Table 5, and assuming for the sake of simplicity that all of the behaviors are equally
indicative of "community" (a proposition open to debate), the Linguist List appears to be
more community-like than the ILF, in that it manifests more community behaviors:
presence of conflict and norms, and active, self-sustaining participation (in addition to the
behaviors that the two environments share). Depending on our initial hypotheses, this
result might be considered surprising: some theorists would predict that the ILF, as a
multimodal environment, would create a richer social experience for users than a text-only
environment (e.g., Media Richness Theory, Daft & Lengel, 1986). Moreover, the ILF was
designed around a system of values (inquiry teaching) that its participants presumably
share. How can we explain the greater evidence of community in the Linguist List?
The dimensions of variation summarized in Table 1 provide clues to interpretation.
Listservs may be more effective at promoting professional development communities than
Web sites, in that the former are "push" technologies and the latter "pull" technologies.
Time being a resource in short supply for most teaching professionals, the convenience of
receiving messages automatically (a medium variable) might make group members more
likely to read and respond to them. The Linguist List also has a pre-existing offline
community—professional linguists who meet face-to-face at conferences and read one
another's work in professional journals, etc.—which provides (and sustains) a basis for
online interaction. Regular off-line contact (a situational variable) may facilitate virtual
community, raising levels of participant trust and emotional investment in the group.
Two other situational factors that conceivably facilitate the formation of virtual
community are the fact that the Linguist List "owners" are peers in relation to the other
participants (all are academic linguists), and that participants are free to select topics of
discussion within the broad theme of academic linguistics, whereas on the ILF the
"owners" and participants are in a hierarchical relationship (university professors and
doctoral students vs. secondary school teachers and undergraduate teachers-in-training),
and topics of discussion in the different areas of the site are more narrowly prescribed. A
sense of shared ownership and empowerment to raise topics of discussion in an online
environment may facilitate virtual community.24 Additional analysis would be required to
determine which of these factors is most explanatory.25
The question of whether the extent of community-like behavior is sufficient to
justify labeling either environment a "virtual community" poses further interpretive
challenges. How "community-like" must a group be in order to be a community? A
researcher could establish objective criteria (e.g., certain key behaviors must be evident, or
a certain combined frequency of a set of behaviors must be found), but this would
necessarily be somewhat arbitrary. Ideally, such an assessment would take into account the
perceptions of the participants themselves: it would hardly be satisfying to pronounce a
Computer-Mediated Discourse Analysis
23
group a community on the basis of empirical discourse evidence, only to find that the
participants themselves did not feel any sense of community-hood.26
At the broadest level, we might make theoretical interpretations about how the
technological and social properties of CMC systems relate to the phenomenon of virtual
community, extrapolating from the observations above. For example, we might use the
comparison of the Linguist List and the ILF to argue against the Media Richness Theory
(Daft & Lengel, 1986), since a lean, text-only environment was found to be more
"community-like" than a rich, multimodal environment (cf. Walther, 1999). The properties
of CMC systems also have practical implications for designers interested in creating
environments to optimize community-like behavior. Designers need to be especially aware
of the ways in which the features of such sites—e.g., push vs. pull message access, co-
present vs. archived past messages, use of visual modalities such as video—encourage or
discourage participation, arguably the sine qua non of community (Herring et al., 2002; but
cf. Nonnecke & Preece, 2000). Finally, our CMDA analysis of virtual community
necessitated the invention of new methods (e.g., coding categories) for identifying and
quantifying communicative behaviors associated with virtual community. This is itself an
original research contribution that could be refined and extended to other computer-
mediated contexts in future studies.
The steps in the CMDA research process and their application to the problem of
assessing the "virtual community" status of the two professional development groups are
summarized in Table 6.
Conclusions
This chapter has presented a methodological overview of computer-mediated discourse
analysis (CMDA), highlighting one empirical, linguistic approach.27 This approach enables
a level of empirical rigor, and reflects a heightened linguistic awareness, that sets it apart
from other approaches to the study of Internet behavior. Five conceptual skills necessary
for carrying out a CMDA analysis using the "code and count" method were discussed and
applied to the concept of "virtual community," specifically the question of whether it exists
in two asynchronous professional development environments. The existence of virtual
community is a fundamental question that needs to be addressed if the term is to be used
meaningfully, rather than purely metaphorically or (in Kling & Courtright's term)
aspirationally, reflecting the user's desire that the positive aspects of community-hood be
attributed to an online group.
Our hypothetical analysis suggested ways in which CMDA can shed empirical light
on the notoriously slippery concept of virtual community. Crucially, CMDA requires that
virtual community be operationalized according to behavioral criteria; on the Internet, such
behavior takes place primarily through discourse. Although there is room for disagreement
as to the best definition of virtual community, an operationalization need only be plausible
and concrete in order to be applied and interpreted. Discourse measures are especially
useful for comparing hypothesized community characteristics in different online
environments or samples of data from the same environment. Further, once virtual
community has been identified by discourse-independent means in some contexts, the
discourse behaviors associated with it can be analyzed and extended as heuristics to
identify virtual community in other contexts.
Computer-Mediated Discourse Analysis
24
Table 6. Summary of the CMDA research process applied to a hypothetical question about
virtual community
CMDA research process
Application to virtual community
Articulate research question(s)
E.g., "To what extent do two online
professional development environments,
listserv X and website Y, constitute
"community"?"
Select computer-mediated data sample
E.g., intermittent time-based sampling
(several weeks at a time at intervals
throughout a year) of public messages from
each group
Operationalize key concept(s) in terms of
discourse features
Community core participants + in-group
language + support + conflict + group
self-awareness + roles, etc.
Select and apply method(s) of analysis
Frequency counts of, e.g., messages and
message length, rate of response ('core
participants')
Structural analysis of, e.g., abbreviations,
word choice, language routines ('in-
group language')
Pragmatic analysis of, e.g., speech acts of
positive politeness ('support'), etc.
Interpret results
1. summarize/synthesize results of data
analysis
2. answer research question(s); explain
unexpected results
3. consider broader implications
1. Listserv X has community features a, b,
c, …; website Y has community features c,
f, …
2. Both have some community features; X
is more community-like than Y. This is due
to …
3. Results have implications for: CMC
theory (e.g., Media Richness); system
design (e.g., push vs. pull access); research
methodology (e.g., coding categories for
community features)
In other respects, virtual community remains a challenging concept to demonstrate.
Operationalizations are inevitably somewhat arbitrary; their value resides in being
empirically testable, not in being true in an ultimate, philosophical sense. But what is
virtual community, really? The concept is derivative of face-to-face community; thus a
comparison between the two would seem to be logically required. However, CMC, by its
very nature, arguably favors different kinds of group interactions than are possible face to
face, causing other circumstances to vary in addition to the modality of the
communication. Face-to-face community and online "community" may not be strictly
comparable (Jones, 1995a); to what, then, can the latter be referenced to establish its
existence? Moreover, the concept of "community" itself is inherently abstract, especially
when stripped of its geographical basis, as is the case in "virtual" community. Whereas
Computer-Mediated Discourse Analysis
25
certain behaviors, such as articulating norms and supporting others, might plausibly be
associated with virtual community status, the same behaviors could also be interpreted in
other ways, e.g., as power negotiation, or strategies to promote personal gain. That is,
concluding on the basis of specific discourse features that a group is a virtual community
might ultimately require too great an interpretive leap, given the abstractness of the target
concept.
To a certain extent, these problems reflect the limitations of CMDA as an
empirical, text-based approach. We can only directly analyze discourse behavior, and must
infer larger social and cognitive formations (such as perceived group identity) indirectly. In
fact, CMDA is most useful for comparing discourse features with independently
established technical, social or psychological phenomena. Thus there are limits on what
kinds of phenomena can be investigated via online discourse behaviors. However, this is
also the case for self-report studies, ethnographic observation, social network analysis, and
indeed for any other methodological approach to analyzing human behavior.
The coding and counting approach to CMDA illustrated in this chapter also has its
strengths and limitations. The approach has the advantage of being based on a familiar
social science paradigm, classical content analysis (Bauer, 2000), the usefulness of which
has been repeatedly demonstrated for the analysis of communication media (Riffe et al.,
1998; see also Bell & Garrett, 1998). It is particularly well-suited to analyzing and
comparing discrete online phenomena, and for revealing systematic regularities in
discourse use. However, quantitative content analysis may not be the best approach for
analyzing complex, interacting, ambiguous or scalar phenomena, which risk distortion by
being forced into artificially discrete categories for purposes of counting. Such phenomena
may be more richly revealed by qualitative, interpretive approaches that illuminate through
exemplification, argumentation and narration.28
The question then arises whether virtual community might more appropriately be
analyzed by qualitative than by quantitative means. Its complexities and ambiguities have
been illuminatingly discussed in ethnographic studies of recreational CMC environments
by Baym (1999), Cherny (1999), Kendall (2002) and Reid (1991, 1994), among others.
The ethnographic approach has been especially revealing in describing insider language
use, rituals, norms and sanctions, and in narrating the histories of these practices. However,
as Liu (1999) notes, most such studies assume a priori that the environments in question
are communities (or in the case of Cherny, 'speech communities'), rather than assessing
empirically the extent to which they meet a consistent set of criteria for community-hood.
As a result, although ethnographic research can provide valuable insights into online
environments in which participants may experience a strong sense of subjective belonging,
the studies do not prove or disprove the existence of virtual community, nor can they be
compared in any systematic way. It seems likely that both qualitative and quantitative
approaches are needed in order to arrive at a full understanding of the nature of the online
social groupings that currently proliferate in cyberspace.
At the same time, computer-mediated groups, including those that meet the
subjective criterion of "feeling" like community to their members, are increasingly
interacting via multimodal interfaces, including Web logs, online videoconferencing, and
navigable virtual reality environments (Bowers, 2000; Kibby & Costello, 2001; Naper,
2001). The CMDA toolkit as articulated here is lacking in methods for analyzing meanings
communicated through semiotic systems other than text. An important future direction for
Computer-Mediated Discourse Analysis
26
CMDA is to identify and adapt appropriate methods of graphical, video and audio analysis
to computer-mediated communication, on the assumption that these modalities
communicate discourse meanings (Naper, 2001; Soukup, 2000; cf. Kress & van Leeuwen,
1996). With regard to online learning environments, Herring et al. (2002) have begun to do
this in analyzing video clips on the ILF site; much more work in this direction remains to
be done.
Acknowledgments
This chapter could not have been written without the feedback from the students in two
graduate Computer-Mediated Discourse Analysis courses I taught at Indiana University in
2001-2002. I am grateful to those students for teaching me through their learning. Thanks
are also due to Sasha Barab, Zilia Estrada and John Paolillo for helpful comments on the
writing and organization of the chapter, and to Anthony Aristar for up-to-date information
about the Linguist List. Any remaining errors of fact or interpretation are my own.
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Notes
1 See, e.g., Burnett (2000), who characterizes "virtual communities" broadly as "discussion
forums focusing on a set of interests shared by a group of geographically dispersed
participants." According to this characterization, almost any Internet discussion group is a
virtual community.
2 For examples of this usage, see Ferrara et al. (1991), who employ the term ‘register’ in
this broad sense, and, more recently, Crystal (2001), who refers to the language of the
Internet as ‘netspeak.’
3 ‘Textual’ is intended here broadly, to include any form of language, spoken or written,
that can be captured and studied in textual form.
4 For a relatively current discussion of ethical issues associated with collecting and
analyzing data from the Internet (although as of this writing, understandings of what is
acceptable practice are still evolving), see Mann and Stewart (2000).
5 Gathering and comparing evidence from multiple analytical approaches is known as
triangulation.
6 The Linguist List has subsequently expanded its Web presence, coming to serve as an
electronic clearing house for language- and linguistics-related resources.
7 This strategy was adopted, for example, by Herring (1992, 1993).
8 This question assumes a common set of criteria for both domains, and the availability of
data for face-to-face communities.
9 Cells above the double line in Table 1 indicate medium (technological) variables; cells
below the double line indicate situational (social) variables (see Herring, u.c. for a full
description of this system of classification).
10 Causal indeterminacy in CMDA research can be minimized in two ways. First, data
samples that are more similar than different can be selected, in an attempt to approximate
the experimental approach of holding all but one feature constant. Second, dimensions of
variation within the data sample(s) can be considered in interpreting the research findings
(see Herring, u.c., and ‘interpretation’ below). In some cases, although differences could
result in principle from multiple contrasting dimensions, in practice, the evidence points
more strongly to one than to the others.
11 In 1997, Linguist made available a new distribution option, Linguist Lite, which sends
subscribers a single message containing the subject headers of the day's messages;
subscribers must then go to the Linguist Web site to read the messages. This option, which
exists alongside the traditional listserv distribution format, combines both "push" and
"pull" elements.
12 Even then, this method is likely to produce more data than can reasonably be analyzed
using most linguistic methods, such that further winnowing of the sample may be required.
13 Among the advantages of ongoing observation is that it allows the researcher to capture
data opportunistically, should interesting interactions take place outside the formally
established data collection periods.
14 For example, chi-squared tests, which compare actual with expected distributions of
results, typically require a minimum of five instances in each sub-category.
Computer-Mediated Discourse Analysis
34
15 For one thing, people can engage in large group conversations online, whereas a
conversation involving one hundred or more people would be impossible face-to-face
(Herring, 1999a).
16 In their study of participation in the video-centered ‘classroom’ discussions on the ILF,
Herring et al. (2002) found that male in-service teachers featured in the videos, and female
ILF development team members, were the most active participants, suggesting that both
status and gender are associated with level of engagement in the site.
17 The criterion of research reproducibility has traditionally been a guiding force in
scientific methodology (cf. Swales, 1990).
18 For an alternative set of criteria, and an attempt to operationalize them empirically, see
Liu (1999), who bases his analysis of community in Internet Relay Chat (IRC) on Jones’
(1997) four criteria for a "virtual settlement:" (1) a virtual common-public-space; (2) a
variety of communicators; (3) a minimum level of sustained stable membership; and (4) a
minimum level of interactivity.
19 The Linguist List has many international subscribers, but most messages are posted in
English, the international language of scholarship.
20 There are several possible reasons why the Linguist List is more conflict-prone than the
ILF, despite the fact that the former is moderated and the latter is not. The Linguist List is
larger and more impersonal than the ILF, which has restricted membership and makes
available individual user profiles. Linguist messages are archived out of sight, while ILF
messages remain on the site. The professional discourse of academic linguists is also
probably more antagonistic than that of secondary school teachers in off-line contexts.
Social accountability, message persistence, and generally supportive professional norms of
communication could inhibit criticism and conflict in postings to the ILF. Alternatively, it
could be that ILF participants are not as engaged in their interactions as are Linguist List
participants.
21 While some of these phenomena are conventionally associated with particular linguistic
means of expressions (e.g., "Thanks" and "I’m sorry" as expressions of politeness), they
can also be expressed indirectly or unconventionally (e.g., "That’s sweet of you" and
"What a klutz I am"). Given the creativity of language users, it is nearly impossible to
predict in advance what all the variants might be.
22 This need not be a problem, provided enough data are analyzed to meet the criterion of
sufficient data to run tests of statistical significance, as noted in the section on "data". If
structural and semantic analyses are conducted of the same data sample, it is possible to
code all of the data for the relevant structural phenomena, and a selected sub-set of the data
for the semantic phenomena
23 Knowledge tends to be expressed as opinions on the Linguist List (Herring, 1996b), and
as advice and personal experience on ILF (Herring et al., 2002).
24 Cf. Bruckman & Resnick’s (1995) suggestion that "letting the users [of a professional
development MOO] build a virtual world rather than merely interact with a pre-designed
world gives them an opportunity for self expression, encourages diversity, and leads to a
meaningful engagement of participants and enhanced sense of community."
25 One direction such analysis might take would be to hypothesize that a given difference is
especially significant, and analyze new data samples that vary only (or predominantly)
according to that dimension. For example, two web-based forums targeting similar
Computer-Mediated Discourse Analysis
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audiences for similar purposes, one created and maintained on a volunteer basis by peers,
and the other created and controlled by "experts", could be compared for evidence of
community behaviors to test the hypothesis that a sense of "shared ownership" facilitates
virtual community. Another possibility would be to conduct multivariate analyses on a
large number of samples that vary according to multiple dimensions.
26 Conversely, participants might experience a sense of belonging and identity even in
groups where discourse behaviors associated with community are lacking. For example,
Nonnecke and Preece (2000) interviewed "lurkers" in online discussion groups and found
that some expressed a sense of belonging, even though they never posted messages to the
group.
27 Interpretive approaches to CMDA, drawing on methods from, e.g., anthropology and
rhetoric, also exist. See, for example, Cherny (1999) and Kendall (2002) for
anthropological (ethnographic) approaches; Gurak (1996) and Herring (1999b) for
rhetorical approaches.
28 Qualitative approaches fall within the purvue of CMDA, provided they are based on
analysis of actual records of online interaction. Examples of qualitative CMDA research,
in addition to those mentioned in note 26, include Baym (1995b); Danet et al. (1997);
Herring, Job-Sluder, Scheckler & Barab (2002); Livia (in press); and Weber (in press).
Moreover, even rigorously quantitative CMDA analysis can benefit from a theoretically-
informed interpretive framework, "thick" description of users, systems and contexts, and
discourse examples to lend analytical nuance.
... Responding to online reviews on a social platform such as TripAdvisor involves computermediated communication (CMC). With the increasing research on virtual communities, Herring (2004) proposes an approach known as computer-mediated discourse analysis (CMDA) to study language use in CMC. This research approach focuses on analysing discourse that occurs virtually in online communication to study CMC online interactive behaviour. ...
... This research approach focuses on analysing discourse that occurs virtually in online communication to study CMC online interactive behaviour. Herring (2004) emphasises that the fundamental of CMDA is the analysis of online interactions in terms of characters, words, utterances, exchanges and so on, which are based on textual and empirical observation. The approach to discourse is therefore the study of language as suprasentential, above the level of the sentence, and within its contexts of use, referred to as little 'd' (Gee, 2018). ...
... Table 1 presents the hotels selected in this research based on the hotel star categories arranged by states. This study applies the use in research approach of Herring's (2004) CMDA. The move structures of main moves and sub-moves in the 72 responses were coded with NVivo 12 into the coding schemes of move analysis according to the hotel star rating using adapted move structures from past studies (Ho, 2017a;Thumvichit & Gampper, 2018), as shown in the table below. ...
... Hierarchical models of linguistic identities hold that authorial identities are reflected at all linguistic levels (Herring, 2004;Grant, 2012;Grant and MacLeod, 2020), yet the relative importance of these elements is seldom empirically explored. In order to understand the contributions of lex-ical distribution, syntactic ordering, or discourse coherence, I test the contributions of different linguistic features to authorship verification by perturbing the input texts. ...
Thesis
Language variation and change are ubiquitous, and one aim of linguistic research is to understand synchronic variation and how it contributes to change over time. This dissertation takes a computationally intensive approach to the investigation of language variation and change, with the goals of 1) understanding the complex linguistic landscape in online communities as a result of variation and change; and 2) developing machine learning-based methods to facilitate the processing of large-scale language data in the form of both texts and speech. The current dissertation reports three case studies on selected patterns of variation and change, which span lexical, stylistic, and speech variation. Study 1 centers on the hypothesis that lexical change in online communities is partially shaped by the structure of the community’s underlying social network. To investigate the relationship between social networks and lexical change, I conducted a large-scale analysis of over 80k neologisms in 4420 online communities spanning more than a decade. Using Poisson regression and survival analysis, this study uncovers several associations between a community’s network structure and lexical change within the community. In addition to overall community size, network properties including dense connections, the lack of local clusters, and more external contacts are shown to promote lexical innovation and retention. Unlike offline communities, these topic-based communities do not experience strong lexical leveling despite increased contact but rather tend to accommodate more niche words. The analysis not only confirms the influence of social networks on lexical change but also uncovers findings specific to online communities. Study 2 takes a deep learning-based approach to studying individual stylistic variation in written texts. The proposed neural models achieve strong performance on authorship identification for short texts and are therefore used as a proxy to extract representations of idiolectal styles. Extensive analyses were conducted to assess how idiolectal styles were encoded by the data-driven neural model. Using an analogy-based probing task, the study shows that the learned latent spaces exhibit surprising regularities that encode qualitative and quantitative shifts of idiolectal styles. Through text perturbation, I quantify the relative contributions of different linguistic elements to idiolectal variation. Furthermore, I characterize idiolects through measuring inter- and intra-author variation, showing that variation in idiolects is often both distinctive and consistent. Study 3 moves beyond textual variation and addresses a methodological bottleneck in speech analysis, that is, aligning continuous and highly variable speech signals to discrete phones. Two Wav2Vec2-based models for both text-dependent and text-independent phone-to- audio alignment are proposed. The proposed Wav2Vec2-FS, a semi-supervised model, directly learns phone-to-audio alignment through contrastive learning and a forward sum loss and can be coupled with a pretrained phone recognizer to achieve text-independent alignment. The other model, Wav2Vec2-FC, is a frame classification model trained on forced aligned labels that can perform both forced alignment and text-independent segmentation. Evaluation results suggest that, even when transcriptions are not available, both proposed methods generate results that are very close to those of existing forced alignment tools. A phonetic aligner for Mandarin Chinese with the same method is also reported. This work presents a neural pipeline of fully automated phone-to-audio alignment to facilitate the processing of the highly variable speech data. This dissertation demonstrates that the abundance of publicly available language data and the advancement of machine learning methods can be effectively harnessed to inform linguistic theories of variation and change.
... Materials posted by non-binary individuals on social network platforms such as YouTube, Instagram, Twitter and Tumblr and focusing explicitly on selfrepresentation were analysed with a view to identifying recurring linguistic discursive patterns as well as potential differences across both individual performers and different media. Methodologically, the study relies on Computer Mediated Discourse Analysis (Herring, 2004) and Systemic Functional Linguistics (Halliday, 2014) tools in order to cover the macro and micro level of linguistic analysis of the data in question. The linguistic choices of users allowed a distinction between "solicited" and "unsolicited" gender performances, as performances on Instagram and YouTube presented relevantly structured elements of discourse which highlighted an index of "stagedness" in the attempt of carrying out an authentic performance in front of a large audience. ...
Conference Paper
Full-text available
Women’s sports are unbalanced in the media compared to men’s sports. This is because the sport press tends to support patriarchal ideologies that reinforce male hegemony in sports and digital media have perpetuated the underrepresentation and gender stereotypes that exist in the traditional sport press (O’Neill & Mulready, 2015; Coche & Tuggle, 2017). The media construct realities through language and the words they choose, but they also reflect the symbolic representations, prejudices and stereotypes that come to influence society, the researcher’s task is to examine and reveal the mechanisms through which these sexual differences are constructed (Van Dijk, 1988; Fairclough & Wodak, 1997). This work analyzes the representation of female and male athletes in the online Spanish sport press during the Rio 2016 Olympic Games to study whether there are imbalances between both sexes. Following the Bednarek and Caple’s (2017) approach to the analysis of news discourse, this study focused on the discursive construction of newsworthiness through text and images that motivate news about female and male athletes. Discourse of News Values allows us to identify what aspects are emphasized or, conversely, hidden, to reveal the way in which such events are packaged to be consumed by the audiences and offer an interdisciplinary and multi-methodological analysis. A selection of the information units is made by means of a stratified random sampling following some pre-established parameters to make the sample to be analyzed as varied and representative as possible and focused on the days of Olympic competition and strictly sports information (39 in total, published between August 15 and 20, 2016 includes 16 women’s and men’s basketball news, 16 athletics also men’s and women’s and 7 profiles). The following analysis categories were established –consonance, eliteness, impact, negativity, personalization, positivity, proximity, superlativeness, timeliness, unexpectedness and aesthetic appeal in visual analysis. An informative interest is revealed marked by a greater masculine superlativity, eliteness and consonance, while the informative interest of feminine events is focused on unexpectedness and timeliness, with a proximity and aesthetic appeal in the visual analysis, which confirms the stereotypical representation of male athletes as being outstanding and well known, while female athletes make the news for the exceptionality of their Olympic results and their appearance.
... Around the world today, the rapidly escalating access to the internet, increased accessibility of internet ready smartphones and other communication devices, as well as the evolution of web-based new media personal websites, social networking sites, blogs, e-newsletters, etc have redefined methods of communication which is leading to a significant shift towards the use of Communication Technologies in everyday human interaction. The CMCT is an umbrella term for all kinds of interpersonal (private and public) communication carried out on the Internet by e-mail, instant messaging systems, mailing lists, newsgroups, web discussion boards, Internet Relay Chat, and web chat channels (Herring, 2004).In the library, they can be used to interact with users in terms of sending overdue notices, ascertaining and mailing needed information, facilitating the process of information acquisition and answering queries. ...
Article
The study examined Computer Mediated Communication Technologies (CMCT) and Librarian’s Interaction with Users for Effective Service Delivery in Delta State University Library. The purpose was to identify the relationship between CMCT and librarian’s interaction with users for effective service delivery. The study adopted a descriptive survey design. The population of the study comprised the total of 56 staff working in the university library (library assistant, library officers and librarians). Census sampling was used to sample the entire population because of its manageable size. Questionnaire served as the primary instrument. Out of 56 copies of questionnaire administered 46 copies valid for analysis. Data collected was analyzed using the descriptive statistics of arithmetic mean (X) and standard deviation (SD). The hypothesis was tested using Pearson Product-Moment Correlation Coefficient. The study found out that there is a strong significant relationship between computer mediated communication technologies like (email, instant messaging, whatsapp) and librarian’s interaction with users for effective service delivery, only that the use of podcast has not been fully recognized in Delta state university library as it shows a very weak relationship significant at(r=0214; p<.05). Hence, the study recommends adequate recognition and incorporation of CMC technologies like podcast by librarians and they should come up other CMC applications to achieve more effective interactionsand efficient service delivery in the library. Keywords: Computer mediated technologies, Services delivery, University library
Chapter
Chapter one sets the scene for this pivot publication. In addition to giving an outline of the monograph’s research objectives and presenting an overview of past research on Wikipedia, this chapter details the theoretical framework motivating the holistic approach to Wikipedia taken in this study. I discuss why it is important to know about Wikipedia’s modus operandi, technological affordances, policies and general site characteristics as well as its position in society and its potential societal functions. In this context, I draw on Herring’s research in Computer-Mediated Discourse Analysis (CMDA) and complement this with a discussion of developments in (Critical) Discourse Studies to underscore the importance of contextualisation and theorisation when examining digital discourse. Finally, this chapter gives a brief outline of the remaining chapters.KeywordsWikipedia(Critical) discourse studies (CDS)Computer-mediated discourse analysis (CMDA)Social media critical discourse analysis (SM-CDS)Contextualisation
Thesis
Full-text available
Summary (English) Purpose: This thesis aims to better understand how individuals working in a virtual organisation can co-create effectively using online collaborative tools, while mitigating the challenges. The research objectives: Identify the most important co-creation activities from a virtual organisation; Define the scope of the open source culture of Space Decentral and explore how it influences aspects of governance and knowledge management; and, Review the gaps in managing activities collaboratively within virtual organisations. Design/methodology/approach: This research uses the literature on the concepts of boundary objects and open source in a cocreation environment to analyze online collaborative tools. Data were collected from conversations and documents found on chat platforms and websites related to the case study called Space Decentral. This material was analyzed through netnography, a relatively new research technique related to ethnography, and text-mining / collection and management of documents from the web. The founding members of Space Decentral wanted to create an open and virtual space agency. Findings: The study of a virtual organisation seems to have led to a different sequence of the co-creation stages and activities established by Frank Piller and Joel West’s 2014 co-creation framework. Members of Space Decentral focused, first, on the Collaborating co creation stage and, then, the Defining one - which is the launch of the co-creation process which seeks to address the problems(s) of engaging external partners in the co-creation effort. Overall, the analysis found recurring themes.They were open source; governance and decision-making; and, leveraging external knowledge - especially within the blockchain community such as Aragon and Ethereum, which include programmers and miners as well. What we found is that online collaborative tools could not “cope” during Space Decentral growth or evolution, especially beyond the structuring of the governance and decision-making framework. Practical implications: The role of virtual organisations as a managerial agency must shift to include both the management of knowledge and expertise, in addition of the management of their online collaborative tools. Résumé (Français) Objectif : Cette recherche vise à mieux comprendre comment les individus travaillant dans une organisation virtuelle peuvent co-créer efficacement à l'aide d'outils collaboratifs en ligne, tout en atténuant les défis. Les objectifs de la recherche : Identifier les activités de co-création les plus importantes d'une organisation virtuelle; définir la portée de la culture open source de Space Decentral et explorer comment celle-ci influence les aspects de la gouvernance et de la gestion des connaissances; et, examiner les lacunes dans la gestion collaborative des activités au sein des organisations virtuelles. Conception/méthodologie/approche : Cette recherche utilise la littérature sur les concepts d'objets frontières et d'open source dans un environnement de co-création pour analyser les outils collaboratifs en ligne. Les données ont été recueillies à partir de conversations et de documents trouvés sur des plateformes de discussions et des sites Web en lien avec l'étude de cas appelée Space Decentral. Ce matériel a été analysé de manière netnographique, une technique de recherche relativement nouvelle en lien avec l’ethnographie, en plus de l’exploration, la collection, et la gestion de texte sur le web. En quelques mots, les membres fondateurs de Space Decentral souhaitaient créer une agence spatiale ouverte et virtuelle. Constats : L’étude d’une organisation virtuelle semble avoir conduit à une séquence différente des étapes et des activités de co-création du cadre de Frank Piller et Joel West en 2014. Les membres de Space Decentral se sont d'abord concentrés sur l'étape de « collaboration », puis sur celle de la « définition » - qui est le lancement du processus de co-création et cherche à résoudre les problèmes impliquant des partenaires externes dans la co-création. Dans l'ensemble, l'analyse a révélé des thèmes récurrents. Ils étaient l’influence de la culture open source; la gouvernance et la prise de décision; et, le fait de tirer parti des connaissances externes - en particulier au sein de la communauté blockchain comme Aragon et Ethereum, qui incluent également des programmeurs et des « mineurs » de blockchain. Ce que nous avons découvert, c'est que les outils collaboratifs en ligne ne pouvaient pas "faire face" à la croissance ou à l'évolution de Space Decentral au-delà de la structuration du cadre de gouvernance et de prise de décision. Implications pratiques : Le rôle des organisations virtuelles en tant qu'agence managériale doit intégrer à la fois la gestion des connaissances et de l'expertise, en plus de la gestion de leurs outils collaboratifs en ligne.
Chapter
O livro “Organizações e Movimentos Periféricos nas Redes Digitais Ibero-Americanas” é um esforço coletivo para retratar diferentes aspectos da atuação de forças sociais no ambiente virtual, sob o prisma das periferias, conceito em plena construção, como ressalta a investigadora Mara Rovida (2020). Estudos geográficos e sociológicos atrelam periférico/a à regiões e a indivíduos afastados dos centros urbanos e dos equipamentos sociais, marcados pela pobreza e segregação (D’Andrea, 2013). Essa mesma periferia geraria uma noção identitária de quem produz o território (Santos, 2002), a ponto de ser um local em potência, dada a dinâmica social poderosa realizada por seus sujeitos periféricos (D’Andrea, 2020). No que se refere à comunicação social, as periferias deteriam o potencial do que Rovida chama de diálogo social solidário nas bordas urbanas, (2020. 6), uma reinterpretação da Solidariedade Orgânica (Durkheim, 1977, in 2004), em dinâmica de cooperação necessária ou interdependência, e da prática jornalística como forma de interação social, ação coletiva e dependente da interação entre sujeitos (Medina, 2014).
Article
This study examines the use of online humour in a subversive local community Facebook group set up in 2017 by disgruntled members banned from a similar group “in opposition to [the original group’s] arbitrarily-applied rules, [its] enforced happiness, and [its] suppression of any post that isn't about giving away lemons or asking to borrow small appliances”. The dissatisfaction with the guidelines and the administration of the original Facebook group provides rich material for humorous posts in the new group, many with varying degrees of aggression directed at the founder and certain members of the “Dark Side”, as the original group is frequently referred to. This article will demonstrate how the use of humour in this new rival Facebook group is used for the purposes of inclusion and exclusion, and how it contributes to a sense of belonging in this online community of practice (Lave & Wenger 1991) created by a small group of self-declared dissidents. It will be shown how the humour shapes the identity of the group through the members’ shared ideologies and beliefs (Tanskanen 2018), and how the humorous messages intended to denigrate and belittle the “Dark Side” reinforce unity among the group members, since the feeling of superiority over those being ridiculed coexists with a feeling of belonging (Billig 2005). Fifteen single comments or multi-post threads were chosen for analysis. These appeared during the first twenty months of this rival group’s existence, and included primarily affiliative and/or aggressive humour (Meyer 2015) directed at the original group. The analysis was carried out using elements of computer-mediated discourse analysis (Herring 2004), and an insider participant-observer online ethnographic approach. The examples chosen illustrate how the humour is used to unite the members of this subversive group by dividing them from the original one, to create the joking culture (Fine and de Soucey 2005) of the new group, and in so doing, creates and sustains the members’ shared identity as irreverent breakaway troublemakers.