Computer-mediated Discourse Analysis (CMDA)

As the thesis will conduct an analysis of extracts from chatlogs in participatory

communities, a computer-mediated discourse analysis will be instructive to understand the online behaviors. Susan C. Herring’s study (2004) stands out as the primary source for conducting this analysis. Herring maintains that CMDA serves as an approach rather than a theory or method

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(Herring 2004:4). Be that as it may, the CMDA approach looks at online behavior through language use and the linguistic perspective, with relation to methodological paradigms like conversation analysis, critical discourse analysis, and interactional sociolinguistics. One should be aware of the fact that a lot of research on online behavior is “anecdotal and speculative, rather than empirically grounded” (Herring 2004:1). The textual trace that a person leaves behind online can only tell what the person does and not what they really think or feel internally, it is therefore sufficient to add a survey or interview in order to get a more broad interpretation of one’s behavior online. In addition to giving an approach to CMDA, Herring provides an

informative way to frame a CMDA related research question and also brings forth the conflict of using ‘community’ to describe interacting online groups.

When framing one’s research question in a CMDA way, Herring maintains the four characteristics to be empirically answerable from the data, non-trivial questions, motivated by a hypothesis, and if it is open ended (Herring 2004:7). One could argue that the research question of finding meaning behind the top five emotes fits all the criterias. The question can be answered through the textual evidence, interpreted data, and logical reasoning. It is also of interest to those who are curious about online behavior on Twitch and the motivation behind comes from the curiosity in finding if there is unison or anomaly in understanding emotes. Herring explains that the problem of using the term ‘community’ comes from the concern of pragmatics, where the meaning of community is rendered meaningless as the virtual community has a fluid

membership, reduced social accountability and lack of shared geographical space. It is therefore difficult for some to say that there can exist virtual communities, however, Herring says it is the researcher’s task to then assess whether or not the virtual online group can be classified as a community. How the pragmatic use of the abstract term ‘community’ affects us, comes from our focus on participatory communites. Later on in Herring’s study, through the use of her literature, a solution emerges where she defines six sets of criteria of a virtual community.

The criteria share some characteristics explained back in the Participatory Community section. The first criterion relates to the active participation with regular participants, a key factor for a virtual third place. The second and third criterion revolve around the shared history, culture, solidarity, and support, to which a Twitch community can gain through the first criterion. Fourth and fifth is the openness for criticism, conflict, and resultion as well as the self-awareness of the

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group that it is in. Both are regulated by the last criterion: the emergence of roles, the hierarchy and governance of a chat. A good harmony of a participatory community on Twitch is when all these aformentioned criteria are met with a functional hierarchy and governance from the moderators and streamer. The CMDA approach has four levels of language that it can study.

Herring defines them as structure, meaning, interaction, and social behavior. A study of Twitch chat will most likely appeal to more than one language level, yet the primary focus will be on the meaning level, followed by a structural level. A meaning level analysis would be the study of what the speaker intends, what is accomplished with the methods of semantics and pragmatics.

The structure level instead looks at genre characteristics, expressivity and complexity through methods of descriptive linguistics and text analysis (Herring 2004:18). Looking at meaning and structure online one can be accustomed to internet jargon, internet lingo, memes, the in-group language but also the exchange of discussion and knowledge.

Herring’s study also brings forth instructive tables that help the CMDA approach to plan out the data sampling. With tables of data sampling techniques, five discourse analysis

paradigms, four domains of language, and discourse behaviors hypothesized to indicate virtual community, one is well suited to begin the research data sampling. When dealing with

interpretations of data, Herring emphasizes taking medium and situational variables into account while also adhering to three levels of interpretation: close to the data, close to the research

question, and beyond the research question (Herring 2004:20). The findings of a research should be issued through generalizability, revisiting the research question and indicate how it has been answered, and extrapolate the strongest possible evidential case for the research. It is therefore essential for this thesis to maintain that its scope only covers findings from participatory

communities on Twitch, a subsection on Twitch with groups ranging from 30-500 viewers, and not covering Twitch.tv as a whole. It is also of importance to mention that when analyzing a chatlog on Twitch, it is a data sampling technique involving a group where the disadvantage may be that it does not cover all participatory communities and that the groups in question are unique compared to the ones left out of the analysis.

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