Coherence in political computer-mediated communication: analyzing topic relevance and drift in chat

Discourse & Communication http://dcm.sagepub.com/

Coherence in political computer-mediated communication: analyzing topic relevance and drift in chat Jennifer Stromer-Galley and Anna M. Martinson DISCOURSE & COMMUNICATION 2009 3: 195 DOI: 10.1177/1750481309102452 The online version of this article can be found at: http://dcm.sagepub.com/content/3/2/195

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ARTICLE

Stromer-Galley and Martinson: Coherence in political CMC 195

Coherence in political computer-mediated communication: analyzing topic relevance and drift in chat

JENNIFER STROMER-GALLEY U N I V E R S I T Y AT A L B A N Y , S U N Y , U S A

ANNA M. MARTINSON

Discourse & Communication Copyright © 2009 SAGE Publications. (Los Angeles, London, New Delhi, Singapore and Washington DC) www.sagepublications.com Vol 3(2): 195–216 10.1177/1750481309102452

INDIANA UNIVERSITY, USA

There is a general perception that synchronous, online chat about politics is fragmented, incoherent, and rife with ad hominem attacks because of its channel characteristics. This study aims to better understand the relative impact of channel of communication versus topic of communication by comparing chat about four different topics. Discourse analysis and coding for topic drift were applied to two hours of chat devoted to the topics of politics, auto racing, entertainment, and cancer support. Findings demonstrate that topic may have an effect on the coherence of chat, with discussion in the politics chat room surprisingly being more coherent than in the other rooms. This research suggests that users can sustain relatively coherent interaction on political talk, suggesting chat technology may not be an inherently problematic medium for political discourse.

ABSTRACT

KEY WORDS:

CMC, coherence, dynamic topic analysis, online discussion, political chat, topic

Using the World Wide Web, one can find blogs, message boards, and chat groups for discussion about a wide range of topics, including politics. Synchronous chat is perhaps one of the more difficult modes for communication online. Scholars of this medium have suggested that synchronous online interaction particularly suffers from a lack of coherence (Herring, 1999; Weger and Aakhus, 2003). The few studies conducted comparing, for example, Usenet’s asynchronous, threaded political discussions and AOL’s pseudo-synchronous chat suggest that chat is the lesser of the two channels (Davis, 1999; Hill and Hughes, 1998) for several reasons.1 The exchanges in pseudo-synchronous chat are quite short; the depth of the arguments produced both within messages and across messages is shallow in comparison to the lengthy messages in threaded, asynchronous forums; there are several near-simultaneous conversations in chat which make it difficult to follow a single topic of discussion; and there seems to be a preponderance of flaming.

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Such concerns about chat are especially pressing when the topic is politics. The discursive model of public opinion suggests that public opinion is formed when citizens discuss public issues and affairs (Price et al., 2002). There is some concern, however, that the evolution of the mass mediated environment in the Western world has turned most citizens from actors into spectators in the political process (Edelman, 1988; Peters, 1995). The Internet complicates this narrative of citizens being cut off from opportunities for public deliberation. Because of its interactive qualities (Bretz, 1983; McMillan, 2002; Rafaeli, 1988; Rafaeli and Sudweeks, 1998; Stromer-Galley, 2004), the software that runs on the Internet enables dialogue among citizens and between citizens and elites (Hacker, 1996). The discussions online serve as a relevant counterpoint to talk offline. Offline, people tend to talk with those who are similar to themselves and hold similar viewpoints (Hays, 1989; Huckfeldt and Sprague, 1987, 1995; Walsh, 2003). Online, there are many discussion groups designed for diverse groups of people to interact with each other (Stromer-Galley, 2002a). However, if chat is a problematic medium for interaction, how then can coherent political discussion be channeled through it? The aim of this study is to investigate coherence when people use the communication technology of chat. Is chat technology inherently a poor medium for holding sustained discussion on political topics? How does the coherence of political talk compare to talk on other topics? To answer these questions, we compared four different topic groups in Yahoo! chat: politics, auto racing, entertainment, and cancer support. We conducted a discourse analysis using a set of categories to measure coherence, including structuring topic and interactional topic. By structuring topic we mean the established (or general) topic for the chat room, which we posit may influence the norms of those who participate (such that some topics will lead to more coherent discussion than other topics), while interactional topic refers to the specific topic(s) negotiated during the ongoing exchange. Our analysis suggests that chat on the structuring topic of politics is more coherent than other topics of chat such as entertainment, health, or sports. We also find that the interactional topics raised in political chat were more likely to be related to the structuring topic than the interactional topics raised during auto racing, cancer support, and entertainment chat. These results suggest that topic may have an effect on the degree of coherence, and opens up the possibility that chat technology is not inherently problematic for sustained discussion on serious topics.

Literature review There is a common conception that political talk online is of poor quality overall. If one follows Sunstein’s (2001, 2003) argument, for example, the picture that emerges is one of radical people finding other like-minded radicals online, then becoming ever more extreme in their positions as they interact. Research about Usenet by Davis (1999) and by Hill and Hughes (1998) suggests that political talk is dominated by a vocal minority who are likely to be anti-government or right

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Stromer-Galley and Martinson: Coherence in political CMC 197

wing, who personally attack the others in the discussion, and who base their arguments on their own opinions with little reference to external sources, such as newspapers or experts, or logical reasoning. Davis (2005) describes online chat quite negatively, writing: ‘Online discussions more closely resemble the Jerry Springer Show rather than National Public Radio or CNN. In political discussions, people often talk past one another when they are not verbally attacking each other’ (p. 67). Synchronous chat seems especially problematic for creating quality interaction, because of its apparent lack of coherence. Weger and Aakhus (2003), for example, argue that discussion in chat rooms is constrained by the technology. Because the chat environment happens in near-real-time yet does not easily facilitate turn-taking, ideal argument cannot occur. Messages are short, leading to under-developed arguments; there is a high level of personal attack; and the overall dialogue is incoherent as multiple people talk at roughly the same time, intermixing two or more separate lines of conversation. For example, if one reads the conversation linearly, an initial message might be about Natasha’s love of dogs, immediately followed by Hiroshi’s agreement that he loves dogs, too, followed by Joaquin’s question about a bug he has encountered on his computer, ending with Shari declaring that NASCAR is her favorite sport. Such an interaction is not sequentially coherent (that is, related messages are not sequenced next to each other in the visual display of the discussion), because turn-taking in a chat environment is assigned by the technology (Herring, 1999). There are limitations of prior research on political interaction in chat. In some cases the analysis is not done systematically – that is, close readings of the interaction are not conducted, but instead a more general impression of the chat is depicted (see, for example, White, 1997). In other cases, analysis is conducted, but not at the level of the message. Instead, it is conducted at the level of the ‘thread’, such that several lines of text are analyzed and judged as a single unit (see, for example, Hill and Hughes, 1998). Both approaches to analyzing synchronous chat may overstate how anti-normative and incoherent the discussion is. For this reason, we turn to research that analyzes chat discussion as interaction. Herring (1999), for example, has investigated the concern that pseudosynchronous chat, by virtue of its structural features, creates an incoherent conversational interaction space. She identifies turn-taking and sequential coherence as the two areas most problematic in chat, explaining that ‘the processes of turn-taking and topic maintenance are subject to disruption and breakdown . . .’ (Herring, 1999: para. 2). Thus, it is difficult for users of chat to follow the unfolding conversation. This potential for a lack of ‘interactional coherence’ seems to be one of the core drawbacks of chat as a medium for interaction (Herring, 1999). Herring’s observations concerning chat highlight the relationship between coherence and topic. While some linguists suggest that coherence is a multilevel phenomenon, encompassing referential coherence, relational coherence, and semantic coherence (see, for example, Moore and Pollack, 1992; Sanders and Spooren, 1999), it can be described more generally as the underlying functional connectedness of a piece of discourse (Crystal, 1992). Coherence is often defined in contrast to cohesion, which describes the lexical, syntactic, or semantic

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connections of linguistic forms at a surface level of analysis (see, for example, Halliday and Hasan, 1976). As suggested by Herring and Nix (1997), coherence is related to the topic of the discussion. If we start from the notion that coherence is related to topic, then topic can be thought of as having two components: the structuring topic and the interactional topic. A structuring topic is established outside of, or before, the interaction. For example, the structuring topic for a reading group might be the ‘book of the month’ selection. The conversation that predominates when the book club meets should thus be about the selected book, although the structuring topic may not be present in all interactions. Running into a friend at the grocery store, and the discourse that results, likely has no pre-established topic. In certain contexts, though, such as business meetings and especially in much online discourse, a structuring topic is likely to exist. Many discussion spaces have pre-established topics, such as the presidential election, auto racing, or a specific current event or news story. An interactional topic, in contrast, is introduced and develops during the course of interaction, what Chafe (1976) and Keenan and Schieffelin (1976) refer to as discourse topic. These topics occur when utterances in an interaction cohere but over the course of the interaction drift, ending up ‘quite far from where [the interaction] started’ (Hobbs, 1990: 3). The reading group that meets monthly, for example, might start with the interactional topic about strengths and weaknesses of the book, then shift to other books by the same author, then to annoying movie versions of great works of fiction. These interactional topics drift at the utterance level through interaction. Structuring and interactional topics likely interact with each other. In the context of interaction where one or more structuring topics have been established externally, they then constrain the interactional topic. For example, within a business meeting, if the conversation shifts from the new advertising campaign, the topic of the meeting, to what is being served at lunch, interactants might declare lunch talk as ‘off topic’. The structuring topic allows interlocutors to manage the topics of interaction, declaring what is relevant or ‘on’ topic and what is irrelevant or ‘off ’ based on the pre-defined, structuring topic(s). The maintenance of the structuring topic throughout the shifts in interactional topic thus constrains the interaction in contexts where there is a structuring topic. In CMC, the coherence of an interaction can be measured by the topics introduced during the interaction (the interactional topics), and by the messages and their relationship to the pre-established topic (the structuring topic). Discussions that move quickly from interactional topic to interactional topic, or that hold discussions on multiple interactional topics simultaneously in pseudosynchronous chat, are more likely to break down. As well, discussions that move off of the structuring topic may drive away people who came to the discussion space to chat on the promised topic, or those discussions may lose focus since they are no longer on the ‘relevant’ topic (that is, the structuring topic) and break down. Research on topic maintenance and change at the sentence level primarily has been conducted on spoken interaction. Chafe (1976) analyzes nouns in sentences as a starting point for thinking about topic. Citing a 1973 conference paper by

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Stromer-Galley and Martinson: Coherence in political CMC 199

Clarke, who distinguished between old information and new information, Chafe explains that a change or migration in topic results from the introduction of new information: Given (or old) information is that knowledge which the speaker assumes to be in the consciousness of the addressee at the time of the utterance. So-called new information is what the speaker assumes he is introducing into the addressee’s consciousness by what he says. (p. 30)

Chafe observes that the way one tells when new information is being conveyed in spoken language is that the voice puts more stress on what is new. Chafe’s research on noun stress is useful when analyzing spoken language; however, analyzing computer-mediated interaction, which is primarily textual, requires computer-mediated communication (CMC) researchers to rely on other cues to determine new and old topics. Research by Keenan and Schieffelin (1976) focuses on how topic is established during interaction, and provides important conceptual work that can apply to both spoken and textual interaction. They analyze child–child and adult–child spoken interactions to identify how topic is coordinated. Like Chafe, they mention the ‘given–new’ distinction (Keenan and Schieffelin, 1976: 337), observing that interactants demand that their fellow interlocutors abide by the ‘given–new’ rules. They explain that successful collaboration on a discourse topic requires that the referent be clearly established. Keenan and Schieffelin (1976) identify four types of discourse topic. The first two fall under the heading of ‘continuous discourse’, and the second two fall under the heading of ‘discontinuous discourse’. Continuous discourse is a ‘series of linked discourse topics’. They explain that ‘the discourse topics are linked in the sense that the propositional content of each is drawn from one or more of the messages already produced in the discourse’ (p. 340). The resulting discourse forms what they call a ‘presupposition pool’ from which are pulled the topics for discussion. In contrast, discontinuous discourse topics are not connected in any clear way to preceding sequences. Keenan and Schieffelin’s (1976) typology of discourse topics is similar to Herring and Nix’s (1997) work on topic shift in online interaction. Herring and Nix investigated two Internet Relay Chat (IRC) groups to determine whether chat could be used for serious purposes, such as pedagogy. Herring and Nix were concerned that chat software would not afford coherent and focused enough interaction for productive pedagogical discussion, where the purpose is for ‘instruction and problem-solving’ (p. 15), as compared with social chat, where the purpose is ‘phatic social interaction’ (p. 15). Their study compared a social chat room to an education chat room using discourse analytic techniques. They conclude that ‘the purpose of communication has a strong effect on the discourse produced, with the distance education chat being more structured and coherent than the social chat, but also more hierarchical’ (pp. 1–2). Herring and Nix (1997) operationalize and modify a scheme developed by Hobbs (1990) that identifies how topic changes over the course of an interaction. Hobbs identified messages as being one of two types: on topic or a shift in topic.

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If the utterance shifts the topic, it does so in one of three ways: parallelism, explanation, or metatalk. Herring and Nix expanded Hobbs’ schema to include a fourth way – a break. They defined parallel shifts as ‘moves [that] include the introduction of different entities with the same properties as those already mentioned, or other properties of the same entities’ (p. 4). Explanational shifts were defined as ‘expand[ing] the topic at hand by explaining a previous proposition’ (p. 4). Metatalk structures the discourse, while breaks change the topic altogether. Herring and Nix (1997) further elaborate on Hobbs’s work by incorporating a measure of ‘semantic distance’. Their coding includes five types or ‘distances’: 1) a message that was completely on topic; 2) a closely related message that is part of the local frame; 3) a related message that is outside of the local frame but part of the global frame; 4) a message that is distantly related, not inside the local frame or the global frame, but ‘related to at least one topic discussed thus far’; and 5), a break, or a message on a completely unrelated topic from any prior local or global frame. The advantage of assigning a distance code is that the conversation can then be analyzed with the Dynamic Topic Analysis (DTA) tool, which creates a visual display of how ‘on topic’ the interaction is over time. A topic with few shifts in topic will display a primarily vertical plot (see, for example, Figure 3); a topic with many shifts or breaks in the interaction will display an image with a more horizontal or fragmented plot (see, for example, Figure 4). Herring and Nix’s (1997) findings show that the pedagogical chat was more focused and there was less topic shift as compared with the sample of social chat. Additionally, their DTA plots shows a marked difference between pedagogical chat and social chat, with clear visual evidence of the greater level of topic shifts and breaks in the social chat. They conclude that, ‘this finding is inconsistent with the claims . . . that pseudo-synchronous CMC is necessarily chaotic and linguistically impoverished’ (Herring and Nix, 1997: 9). They suggest that one reason the social chat was so much more prone to topic shift was the absence of a group leader to facilitate the discussion and to create structure for the interaction. Continuing the work by Herring (1999), Herring and Nix (1997), and by Keenan and Schieffelin (1976), in this article we further analyze topic shift in synchronous chat environments. The above research raises the question: how do different purposes for interaction online (as reflected in the choice of structuring topic) influence the coherence of the discussions as examined through the lens of the interaction topics? Comparison is an important tool for identifying differences between two or more interactions. For that reason, we sought to compare chat across four different structuring topics, two with relatively serious purposes (i.e. politics or providing online support to people with cancer) and two with relatively social purposes (i.e. automobile racing or entertainment in general). The question remains whether serious chat and social chat might be generally on the structuring topic. Given that the purpose of cancer-support chat should be to share stories and provide information to those who have cancer, the potential exists for the interactional topics to be closely tied to the structuring topic. Similarly, since the topic of automobile racing is likely to appeal to a select sub-group of

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sports fan, their discussion also should generate highly focused interaction about drivers, their strategies, recent races and the like. In contrast, a fairly general structuring topic such as ‘entertainment’ should exhibit more breaks and shifts in the interactional topic, because the structuring topic is too general to provide focus for the discussion. The structuring topic of politics is similarly quite broad, and given prior research on political chat, we expect it to exhibit similar characteristics as that of the general topic of entertainment.

Method Although political AOL chat spaces primarily have been studied in the past (see, for example, Davis, 1999), observations of AOL chat and Yahoo! chat suggest that Yahoo! has a more active group of participants and wider range of channels than AOL (Stromer-Galley, 2002b). Accordingly, Yahoo!’s chat spaces were chosen for the purposes of this study. Yahoo!’s chat rooms are organized by topic. A person who enters a chat room can choose from 18 overarching categories that range from ‘Computers & Internet’ to ‘Romance’ to ‘Government and Politics’. Each of the 18 categories contains sub-categories. For example, ‘Computers & Internet’ has 11 sub-categories for such topics as ‘Yahoo! Help’, ‘Electronics’, and ‘Web Design’. A sample of politics chat was recorded from the ‘Politics Lobby’, an all-purpose politics chat room in the Politics and Government category. Cancer support chat was recorded from the ‘Cancer Support’ room in the Health category. For the more social categories, the ‘Entertainment Lobby’ was chosen, an all-purpose entertainment chat room in the Entertainment & Arts category. Finally, ‘Auto Racing’ in the Sports category was selected. For the purposes of this comparative study, two hours each of auto racing and cancer support talk were recorded on 23 February 2004, and two hours each of political and entertainment talk were recorded on 26 January 2004. The results reported in this article are from the first 500 ‘lines’, approximately onefourth or the first half-hour of the discussion for each session. ‘Lines’ included unique messages entered from each participant, as well as system messages announcing a person entering or leaving the chat room. The data were analyzed in accordance with principles of interpretive discourse analysis (Herring, 2003). A codebook was developed to operationalize the measures for interactional topic and structuring topic. The coding was done at the level of the chat message. A chat message is a sentence, sentence fragment, or set of sentences that a participant types and then hits ‘enter’ on the keyboard to have the thought entered into the discussion for others to see. Each chat message in the discussion was assigned two codes, one for interactional topic and the other for structuring topic (see Appendix A for the codebook). A message was coded for structuring topic either being relevant or not to the topic as established by Yahoo! Thus, for example, the message was coded as being relevant to the structuring topic in the politics chat room if the message focused on campaigns, candidates, public policy, foreign affairs, current events,

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political figures, or social issues. For the auto racing chat, if the chat message was focused on races, drivers, cars, repairs, or fans, it was coded as being relevant to the topic. For cancer support, it was coded as being relevant to the structuring topic if the message was on the topic of cancer or was providing messages of concern or advice. Finally, messages recorded in the entertainment chat were coded as being relevant if the discussion was about any kind of popular or high culture (books, movies, television, music) or media for channeling culture. For the structuring topic code, the researchers were in agreement 98 percent of the time. Herring’s (2003) and Herring and Nix’s (1997) dynamic topic analysis coding techniques were adapted to measure how coherent the discussions were based on the interactional topic. The objective was to operationalize and measure the drift in topic across messages. Krippendorff (2004) explains that when conducting content analysis, it is important to develop and test the coding with more than one person in an effort to establish intercoder agreement. This helps to improve the chances that the code assigned to the discourse or the phenomenon has face validity and can be more confidently established as a reliable measure of the phenomenon of interest. Thus, we adapted rather than applied the Herring (2003) and Herring and Nix (1997) coding scheme, because although we found the idea of coding for topic drift and then visualizing that drift to be compelling as an analytic technique, we had difficulty applying the codes described in the Dynamic Topic Analysis (DTA) coding scheme.2 In the original DTA model, as described earlier, a message was coded for its topic development – parallel, an explanation, metatalk, or a break – and for its semantic distance from a prior, related message on a scale from 0 to 4. We had difficulty differentiating parallel shifts from explanatory shifts. We had even greater difficulty agreeing on the semantic distance – especially when trying to determine whether a shift occurred within a local or global frame.3 Hence, we had to step back and reconsider how to measure topic drift. We eventually decided to return to Hobbs’s (1990) notion of old and new topic in order to focus first on whether the message was on the old interactional topic or raised a new interactional topic, and second whether the message used an ‘old’ referent or introduced a ‘new’ referent.4 This led us to develop a 2 × 4 table as depicted in Table 1. In our observations of the discussions, we found that, for example, a political conversation in the politics chat room can flow from public opinion polls, indicating that a majority of United States citizens favor Bush’s handling of the Iraq war (in 2004) to thoughts about Saddam Hussein and debate about the consequences of sanctions in Iran and Iraq, to arguments about whether Hussein was a threat to the US. This wide-ranging conversation is viewed in this research as being on the same interactional topic. All of these subjects related to each other in a coherent fashion, because they deal with a larger topic of the United States’ war in Iraq. Thus, the interactional topic is not Bush or Hussein or sanctioning Iran and Iraq. Those terms are referents of the same overarching topic. However, if the flow of conversation switched from debating the purported liberal or conservative bias of the media to accusing another participant of being ‘a radical commie pinko’, the interactional topic shifts from liberal and conservative perspectives to a personal attack. While sharing a reference to

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Stromer-Galley and Martinson: Coherence in political CMC 203 TA B L E

1 . Matrix of coherence for interactional topic with corresponding drift

Continuous topic Discontinuous topic

Topic

Referent

Drift

Old topic Old topic New topic New topic

Old referent New referent Old referent New referent

No drift Small drift Greater drift Break

political discourse, the topic has shifted from the old interactional topic of Bush’s handling of the Iraq war to a new interactional topic of a personal attack and thus is not as closely related as the previous discussion of Bush and Hussein and sanctions and threats. The old referent/new referent categorization allows one to look more closely at a specific chat message and determine whether a message shares the same nouns or synonyms for those nouns as from a prior message. A conversation that focused on Hussein and why he is or is not guilty of war crimes followed by a message that ‘Hussein rhymes with insane’ shares the same referential component, Hussein, although the second message is not on the interactional topic of whether Hussein engaged in war crimes, but rather constitutes a form of word play. Without close attention to the referential component, it might appear that the two messages are more closely related than they are because of simple word repetition. Considering both the interactional topic and the referential component allows the coder to address what we think are two key aspects of topic drift. The resulting coding scheme utilizes a forced choice yes/no decision process that we hope may be utilized by researches working across disciplines (such as the current authors coming from political communication and information science). Thus, the coding scheme developed establishes the amount of drift in the interactional topic during a chat conversation, as exhibited in Table 1. If a message was on the same interactional topic, we determined whether there was a new or an old referent focusing primarily on the subject nouns in order to determine how much interactional topic drift was occurring. Thus, if no new referents were introduced in the message, such as with phatic communication or one-word answers to questions, such as ‘yes’, then we viewed that message as having no topic drift (code of 0). For example, if a person entered the discussion channel and greeted another, and that greeting was acknowledged with a greeting back, the interaction was coded as being on topic. Messages with a little topic drift (code of 1) were those that continued the old topic and introduced new referents. Generally these were messages that provided some elaboration on a prior message. To give an example, in political chat, two people discussed the war in Afghanistan.5 (1)

a. Christa b.

Groovy

“Hey I just saw on CNN talking about finding ‘low load’ nukes in the caves” “Chris; . . . you sure you heard that right? This is just another example that Bush was right to go into Afghanistan.”

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Christa explained that CNN reported finding a particular kind of nuclear bomb in the caves of Afghanistan where the Taliban and Osama bin Laden were reported to have fled. Groovy replied with a message that is on the topic and which introduces a new referent. Groovy restated the information reported from Christa in the form of a question, indicating a continuation of the topic and the referent of nuclear bombs in Afghanistan. Groovy then introduced a new referent, President Bush, and made a claim about Bush making the right decision to send military troops to Afghanistan. Chat messages that introduce a new topic, but which may or may not carry an old referent forward, suggest further drift (code of 2). Interactions that offer a new topic but an old referent are generally an interaction that plays with words or that shifts from talking about the topic to talking about the person, as in the initiation of an ad hominem attack. An example of word play that changes the topic but continues the referent is the following: (2)

a. Fastbatard b. Zoinks c. dk19

“my youngest son and you are like twins” “whose twins?” “my balls are twins”

Fastbatard in a prior exchange told dk19 that he was being immature, then compared dk19 to his youngest son. Zoinks’ message was a question that continued the old topic and old referent. dk19, however, took the referent of ‘twins’ and played on that word to make a dirty joke, thereby shifting the interactional topic. The most obvious discontinuation of a topic is a message that introduces a new interactional topic along with a new referent (code of 3). In these interactions, a message might be a question on a new topic. If, for example, three people were sharing their emotional reactions to first learning they had breast cancer, and a fourth person asked ‘Does anyone know of a good website that lists hospitals?’, such a question would not continue the topic of initial breast cancer diagnosis nor would it carry any referent forward (so long as the prior messages had not mentioned hospitals). Thus, in our coding we considered a message to be completely on the topic (no topic drift) if the message continued the prior interactional topic and used key terms from prior messages (e.g. greetings and responses to greetings). An utterance was coded as having some topic drift if it continued the existing interactional topic, and introduced a new referent (e.g. a question requesting additional elaboration). However, if the utterance introduced a new topic while carrying forward an old referent (e.g. word play or personal attack), then it was viewed as encompassing more drift in topic. Finally, if an utterance introduced both a new topic and a new referent, then it was considered a break in the interaction. For the interactional topic code, the researchers were in agreement 80 percent of the time. After coding the messages, we used the VisualDTA tool developed by Herring and Kurtz (2006) to provide a visual map of the conversation in the four chat rooms. This software application creates a dynamic visual display of data that have been analyzed with the set of alphanumeric codes used in DTA (see Figures 1–4 for examples of static screen-captures of data represented using VisualDTA).

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We note here that in order to use the Herring and Kurtz visualization tool, we had to translate our coding system back into the DTA coding system. This is not ideal, since there is not a 1 for 1 correspondence between DTA and our coding for topic drift. For example, Herring’s system has five ‘distances’, and our coding system only has four. Nevertheless, we chose to make this translation so that we could apply what we consider a useful analytic tool, visualizations, to convey topic drift. We translated our codes to the letters in the Herring and Nix visualization scheme in the following ways: old topic/old referent = T (no distance); old topic/ new referent = P (distance of 1); new topic/old referent = P (distance of 2); new topic/new referent = B (distance of 3).

Findings The first analysis focused on whether chat in each group stayed on the structuring topic as established by Yahoo! When we examined the text of the messages, different patterns emerged for each chat room. As Table 2 shows, the overwhelming majority of the messages in the politics chat (94%) were politically oriented. By contrast, the majority of messages in the other chat rooms were not oriented to the topic for the room. In the auto racing chat room, only 22 percent of messages were about auto racing. Similarly, in the cancer support chat, only 16 percent of messages were on the structuring topic of cancer or explicitly supporting other participants with their struggles with cancer, and in the entertainment chat lobby, a mere 6 percent of messages had content that was explicitly about popular culture, film, radio, or television. The second analysis focused on interactional topic and tracked the degree of topic drift. As Table 3 illustrates, we found that political chat had the greatest TA B L E

2 . Percentage of discussion on the structuring topic Auto racing N = 360 percent

Off topic On topic Total TA B L E

Cancer support N = 398 percent

Entertainment N = 410 percent

Political N = 385 percent

93.9 6.1 100

6.2 93.5 100

83.9 16.1 100

78.3 21.7 100

3 . Percentage of discussion on the interactional topic Auto racing N = 360 percent

Old topic, old referent Old topic, new referent New topic, old referent New topic, new referent Total

56.9 5.3 5.8 31.9 100

Cancer support Entertainment N = 383 N = 410 percent percent 75.7 .5 .3 23.5 100

70.7 3.4 13.9 12 100

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Political N = 384 percent 79.4 3.6 12.5 4.4 100

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percentage of messages that were on an established interactional topic and old referent components (79%), followed closely by cancer support chat (76%) and entertainment-oriented talk (71%). In contrast, in our sample of auto racing chat, a smaller percentage of messages were both on an old topic and semantically connected (only 57%). Additionally, there are noticeably different patterns across the four types of chat when considering the topic drift codes (same interactional topic with new referent, new interactional topic with old referent, and new interactional topic with new referent). Messages that introduced a new interactional topic while retaining a referential connection with prior messages occurred in both political and entertainment chat (13% and 14% respectively). In the politics chat, new interactional topics introduced with an old referent usually indicated a shift from talking about the topic, for example the war in Iraq, to a personal attack on one of the other participants. In the entertainment chat, these shifts were rarely personal attacks, but instead constituted word play or puns. However, for auto racing and cancer support chat, the second most common pattern was to introduce a new interactional topic with no referent connection to prior messages (32% and 24% respectively), in effect introducing a break in the conversation. Examination of the text of the messages indicated that participants in both auto racing and cancer support chat paid a great deal of attention to, commented on, and welcomed other participants as they entered the chat room. For example, in the auto racing chat room for a short time a few people were experiencing technical difficulties with Yahoo! and were being kicked involuntarily out of the chat room and then re-entering. Upon re-entry, many of the active discussants would greet the person, creating a break in the current discussion. We then used the visual display offered by the VisualDTA tool to map the interactional topic of the conversations. The y-axis represents the conversation over time message by message, with each number representing the next message as in the transcript of the chat session. The x-axis represents distance over time as messages stay on topic (represented by the letter T for on topic), shift the interactional topic (represented by the letter P for parallel shift), or break with the previous topic (represented by the letter B). The NA (not applicable) code was given to automatically generated system messages such as indicating that a participant has joined or left the room. As Figures 1 to 4 show, differences exist between and across the four groups. The visualizations provide a useful analytic tool for depicting the unique patterns that emerge in each discussion. Although we do not claim that these visualizations are representative of the data, since this is not a sample, they are useful illustrations of the discussions. As well, as we note in Table 3, the proportion of distance codes (old topic/old referent, old topic/new referent, new topic/old referent, new topic/new referent) for the entire sample that we coded further confirm the patterns in the visualizations. The politics chat, as depicted in Figure 1, appears to have a high degree of coherent interaction. That is, most of the messages are replying to or interacting directly with a prior utterance, as indicated by the lines connecting the messages.

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Stromer-Galley and Martinson: Coherence in political CMC 207

FIGURE

1 . Representation of a sample from political chat using visual DTA

FIGURE

2. Representation of a sample from auto racing chat using visual DTA

The presence of a line indicates that there is a connection via either the same interactional topic or the use of the same referential component between the two messages. There also is some referencing back to immediately prior as well as more distantly prior messages, suggesting that participants are interacting with multiple people and on multiple points. Some of the discussions stay tightly connected to the topic, as suggested by the vertical line. The lines that move more

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Discourse & Communication 3(2)

diagonally across the page are messages that are less connected to the messages to which they are referring. In stark contrast is the auto racing chat, as depicted in Figure 2. The stuttered vertical lines suggest short chains of messages that are on topic, followed by frequent breaks in the conversation. This pattern is common for greetings of new

FIGURE

3. Representation of a sample from cancer support chat using visual DTA

FIGURE

4. Representation of a sample from entertainment chat using visual DTA

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Stromer-Galley and Martinson: Coherence in political CMC 209

entrants into the chat room and was a frequent occurrence in both the auto racing and cancer support chat. The cancer support chat shows a somewhat similar visual pattern as the political discussion (Figure 3). The cancer support discussion also appears to be fairly coherent and on topic, although a new conversation starts at line 19 that is not connected to any prior messages, continues for six messages and then is abandoned. The entertainment chat (Figure 4) is illustrative of a chain of interactions that are only loosely connected; the messages are referentially connected, but the interactional topic keeps shifting from utterance to utterance; there is greater topic drift in the entertainment chat than in the cancer support or political chat. This is represented visually by the more diagonal stepping of the DTA visualization.

Discussion Taken together, these findings strongly suggest that both structuring and interactional topic matter, regardless of constraints introduced by the underlying chat technology. Each chat room exhibited a characteristic pattern of topic development (e.g. the use of greetings, personal attacks, or semantic play). More specifically, we found that the political discussion was more coherent and more likely to be on the structuring topic than any of the other topics. The political conversations lasted longer and appeared to be more coherent overall than the conversations occurring on the topics in the other chat rooms. In our sample, the main positive aspect of political chat was that it was the only topic on which participants remained consistently engaged (94% of messages were about politics). These results offer a starkly different picture from that of prior research on political chat and from our own predictions. Rather than incoherent, disengaged discussion where people ‘talk past one another when they are not verbally attacking each other’ (Davis, 2005: 67), we find a relatively coherent and sustained interaction on political topics. This suggests that the chat environment may not be inherently problematic for talk on a political matter, and that users will find ways to work around the limitations of the technology to sustain a conversation. The results of the analysis of the 500 lines of chat that we coded from the four chat rooms support Herring and Nix’s (1997) finding that ‘serious chat’ (i.e. pedagogical chat) is more likely to remain on topic than social uses of chat. People in the politics chat room stayed on the structuring topic rather than move to other topics. This type of ‘on topicness’ was not the case for the other chat rooms we observed. The fact that politics and cancer support chat were more coherent interactionally in comparison to the less serious topics of auto racing and entertainment suggests a relationship between structuring topic and interactional topic. The purpose of the discussion likely influences how coherent the group’s discussion is. Serious topics may call for a more engaged and involved discussion, encouraging participants to focus on prior messages and continue the topic from prior turns. The second positive aspect of the politics chat was that those who participated said comparatively more than those in the other topic rooms. A simple word

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210 Discourse & Communication 3(2)

count indicated that participants in the politics chat room said far more than those in the other chat rooms (see Table 4). Although this study did not measure the quality of the actual messages, the evidence indicates a fairly elaborate and engaged conversation on the topic of politics in the chat room. One strength of this research project is that the coding was done at the level of the message. As noted earlier, prior research on political chat coded at the level of the thread, which means that a set of exchanges would be treated as a single unit and then coded for level of ‘deliberativeness’ (see Hill and Hughes, 1998, for an example of this macro-level analysis). A drawback of analyzing a thread is the amount of information about the interaction that gets lost when analyzing only at a macro-level. What might appear to be deliberative, for example, because two people are disagreeing with each other, upon closer examination might not be deliberative, because the two people are on their own separate tirades and not engaging each other. More studies need to move to analyzing the interaction, coding and assessing at the level of the message rather than at the level of the thread. There are some limitations to this research that will require further work to address. The first is our intercoder agreement scores. Although on the ‘structuring topic’ codes our percent agreement was quite high (98%), for the ‘interactional topic’ code, the percentage agreement was much lower (80%), and when one accounts for chance using the Cohen’s Kappa statistic, the agreement score is .5 (50% agreement when accounting for chance). The interactional topic coding scheme, as mentioned earlier, was adopted from discourse analytic research. Discourse analysis is an interpretive approach to studying interaction, and the interactional topic coding is highly interpretive. In discourse analysis, an 80 percent agreement may be acceptable for analysis that involves a high degree of interpretation on the part of coders. Nonetheless, we believe further work on topic shift could provide additional decision rules that would result in more robust measures of intercoder agreement. A second concern is whether the data we have analyzed are ‘typical’ for the particular topic-oriented chat rooms we studied. We studied 500 messages produced in each chat room. Although we witnessed a good deal of turnover, with new participants entering and other participants leaving the discussion, there were consistent voices through the entire sample. As interpersonal and normative research suggests (Deutsch and Gerard, 1955), individuals help set the norms for the room. It might be that on a different day or at a later time we would see

TA B L E

4 . Number of words per group Auto racing

No. of words No. of messages No. of people Avg. no. of words/message Avg. no. of words/person

1606 440 40 4 40

Cancer support Entertainment 1680 445 39 4 43

1446 475 22 3 66

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Political 4945 476 26 10 190

Stromer-Galley and Martinson: Coherence in political CMC 211

a change in the norms as entirely new people populate the rooms. Thus, more coding is needed, both of a longer duration (perhaps 2000 messages) and on different days so that we can gain further confidence that the differences we observed are typical for each chat room.

Conclusion It is still an open question whether political deliberation online can approach ideals of political discussion among citizens. What this research suggests, though, is that the medium may not necessarily be an insurmountable obstacle to promoting good discussion. Deliberative political discussion does seem possible with chat technologies, at least in the sense that the discourse can be fairly coherent. Sustained, engaged interaction on a topic is an essential component for healthy political discussion, and our research suggests that political chat can meet that requirement for good discussion. Measuring coherence in chat is difficult. The Visual DTA tool is a potential resource to help visually map chat interactions in order to observe larger patterns that could be missed when only coding and counting the interactions. More mapping of chat is needed to help provide a baseline of data and to serve as points of comparison (see, for example, Zelenkauskaite and Herring, 2008). If we are to determine how the medium of chat affects interactions, we must continue to look across topics, at different times, as well as across different cultures to see how the medium interacts with the message. APPEN DIX A: CODEB O OK FOR CODI NG STRUCTU R I NG AN D I N TE RAC TI O N A L TO P I C

The interactional topic code The objective of the distance code is to measure how ‘on topic’ or ‘related’ subsequent messages are to prior messages. This code cannot (and perhaps no code can) capture the full complexity of how related subsequent messages are to prior messages. The purpose of this is to provide a sense of when there are significant topic drifts in an exchange. To capture this, we code for two interrelated categories: topic-relatedness and referent-relatedness. Topic-relatedness is the first variable. Topics can be thought of as umbrellas for linked knowledge structures. For example, a clear political conversation can flow from polling to Bush to Al Qaeda to Hussein to policy and consequences of sanctions of Iran and Iraq, to debating whether Hussein was a threat to the US. This wide-ranging conversation is viewed in this research as being of the same topic. All of these subjects are related elements of a larger topic that has a key connection with foreign policy and how to determine and deal with potential threats to the United States. Thus, the topic is not Bush or Hussein or sanctioning Iran and Iraq. Those are components or elements of the larger topic. Referent-relatedness is the second variable. Referent-relatedness is when a subsequent message shares the same semantic frame as a prior message. A conversation that focused on Hussein and why he is or is not guilty shares the same frame of reference, which is Hussein. Something that introduces a new referent introduces something that was not in the prior message, but is still topically related. If a subsequent message makes a link between and old referent and a new referent, however, it remains a 0, because the message makes an explicit link between the two referents.

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212 Discourse & Communication 3(2) To code for drift, then: 0 = old topic/old referent 1 = old topic/new referent 2 = new topic/old referent 3 = new topic/new referent 0 = A zero code means that a message is both on topic and continues the referent. In addition, a continuation of a prior message by the same person generally will be coded as being 0 distant (see exception in ‘2’). Name

Code

Message

Sue Teri

na 0

George Bush is Great! I agree

1 = A 1 code means that a message continues on topic, but it introduces a new referent. Bill’s utterance, below, is making an implicit link between George Bush and the war in Iraq. Thus, a new referent is introduced that is about the war in Iraq, yet it is still on topic, because there is an implicit link to an evaluation of George Bush. Name

Code

Message

Sue Teri Bill

NA 0 1

George Bush is Great! I agree I’m not sure the war in Iraq is a good idea

2 = A 2 code means that a message continues the referent, but is no longer on topic. This is a unique class of utterance that is most likely to be seen when an utterance shifts from talking about a topic that is something in the world to discussing a person in the interaction or a shift from talking about a topic to playfulness or responding to a behavior. (Note: LOLs or other short utterances do not count as playfulness or response to behavior and remain 0s.) Name

Code

Message

Sue Teri Bill

NA 0 1

George Bush is Great! I agree Sue, how can you say that? (or: Sue, are you a Republican?)

3 = A 3 code occurs only when there is a complete break in the interaction. If a participant enters the room and greets another participant or introduces a new topic (since she did not know what the prior conversation was about), that would likely receive a ‘3’. Or, if a participant just decides to change the subject without any clear connection to prior utterances, then it receives a 3. Name tallyuk

Code 0

Message Gest.... I belong to amnesty international.......and its the official line.....you tell me how Iran managed all those years without children dying... and in the same sanction..... all these children are dying in Iraq/

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Stromer-Galley and Martinson: Coherence in political CMC 213 (Continued) Name

Code

thechick titanium

0 0

thechick tallyuk

0 0

darn21 thechick darn21 thechick

3 0 0 0

Message http//www.againstbombing.com/7Washlies.htm hey cable chick-children are dying all of the world from the runs so that makes it ok??? titan? Groovie..... why is it always CHILDREN dying in this proporganda......dont you get the tinyest bit suspitous about it? cable chick do u work with cable darn; yeah I do.. Where Minnesota

The structuring topic code This code captures whether the message is ‘on topic’ relative to the structuring topic of the chat room. So, for example, in the Yahoo! chat room that bears the name ‘political chat’, are the messages in that room on politics? Similarly, in the Yahoo! chat room that bears the name ‘auto racing’, are the messages about auto racing? Each utterance must be coded for whether it is on the topic announced for that room or not. 0 = an utterance that is not on topic 1 = an utterance that is on topic. Yahoo! Politics Chat: Name

Code

Message

Observer

0

I guess GOD is crying

badvan

1

Yoe How hard would it be to have an agent do a drive-by on a guard? It would cost the price of a bullet and some gas; but the benefits are myriad. You have to ask yourself who benefits and don’t assume you aren’t being lied to. It's a savage world. We are pawns.

yoep

1

besides that the fbi “claims” it wasent terroist related

cshard

1

Jason it is common knowledge that Saudi had more to do with 9/11 than Afghanistan did.

Auto Racing Chat: Name

Code

Message

Shadowz

0

Playing Zebra – Tell Me What You Want

evilgrinchie

0

bite me not for long

Chuck

1

i’m not a 17 fan but he kicked ass yesturday

evilgrinchie

0

wb nony

umpire45f

1

that was a close finish yesterday

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214 Discourse & Communication 3(2) N OTE S

1. We recognize that chat technology is not truly synchronous. Discussants cannot interrupt each other, for one thing. For another, the experience of chat, especially as it compares to threaded discussion, is that it is near-simultaneous. A person types a message, hits the ‘enter’ key, and the message appears in the chat window for the others to see. There is, however, a time delay as the message is relayed through the network and to the server for display. 2. It should be noted that a recent iteration of the Visual DTA (Zelenkauskaite and Herring, 2008) eliminates the metatalk and explanation categories. 3. Zelenkauskaite and Herring (2008) have abandoned the notion of local or global frame, and replaced it with categories of ‘immediately obvious’, ‘clear with a little thinking’, and other perception categories of the coder. 4. The notion of analyzing the message for their topic and their referent was a suggestion by Robert E. Sanders. We owe a debt of gratitude to him for suggesting this way of thinking about how messages relate to each other. 5. Names of participants have been changed to protect their anonymity. REFERENCES

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J E N N I F E R S T R O M E R - G A L L E Y is an Assistant Professor in the Department of Communication at the University at Albany, SUNY. Her research interests include: the uses of communication technology and its implications for democratic practice; mediated political campaign communication; and deliberative democracy. Her research has appeared in the Journal of Communication, The Information Society, Javnost/The Public, PS: Political Science and Politics, Journal of Computer-Mediated Communication and Journal of Public Deliberation. She is currently the a co-principal investigator on the Deliberative E-Rulemaking Project, a NSF-funded project to apply natural language processing and multi-level deliberation to federal agency online rulemaking. A D D R E S S : Department of Communication, SS 340, University at Albany, SUNY, 1400 Washington Ave, Albany, NY 12222, USA. [email: [email protected]] A N N A M . M A R T I N S O N is a doctoral candidate in Information Science at Indiana University specializing in gender, discourse, and information technology. Her dissertation research focuses on ideological debates about feminism on the Web. Anna has published in the areas of IT and women’s leisure; feminist science fiction; and (with Susan Herring) the representation of gender on websites. Her publications have appeared in Extrapolation, the Journal for the American Society for Information Science and Technology, the Journal of Documentation, the Journal of Language and Social Psychology, and New Media & Society. [email: [email protected]]

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