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<Paper uid="W06-3410">
  <Title>RETROSPECTIVE ANALYSIS OF COMUNICATION EVENTS - Understanding the Dynamics of Collaborative Multi-Party Discourse.</Title>
  <Section position="4" start_page="62" end_page="66" type="metho">
    <SectionTitle>
2 Method
</SectionTitle>
    <Paragraph position="0"> The design methodology we used included a review of the literature, folowed by in-depth focus group discusions with working analysts to determine requirements. Folowing the group sesion, a participatory design process was used to gather more information from our user group, leading to a set of sketches that were used in the initial prototype implementation. From these an initial prototype was created. The test-bed is currently in its second phase of implementation that includes the integration of components developed under other auspices into the RACE environment. These include indicators of affect and social roles. Finally, we have built and colected a number of data sources that we intend to use to evaluate the system. We describe these stages in the folowing sections. null</Paragraph>
    <Section position="1" start_page="62" end_page="63" type="sub_section">
      <SectionTitle>
2.1 Prior Art
</SectionTitle>
      <Paragraph position="0"> This effort began with a thorough literature review acros the fields of ubiquitous computing, visualization, multi-party discourse and communications theory. A number of research systems with similarities to our goal were reviewed in order to be able to understand the landscape and determine where specific oportunities may lie. Here we discus some of the systems (mainly research prototypes) that are available for reviewing communications data.</Paragraph>
      <Paragraph position="1"> As both Internet communications and complex graphics capabilities have become more pervasive in modern computing, there has been much interest in visualizing conversations. Due to the ease of data capture with computationally supported communications, such communication modalities as email, chat, and forum/newsgroup threads appear to be the most researched. Several systems have represented vast, multi-threaded newsgroup or forum posts such as USENET.</Paragraph>
      <Paragraph position="2"> 'Lom' can represent the activity patterns of individuals relative to one another, helping to characterize individuals' participation and roles (Donath et al, 199). In another view, linked posts are graphed to represent threads, characterizing the newsgroup as a whole. 'Discourse Diagrams' describes newsgroups with semantic graphs of related concepts, and also graphs people's conectedness to one another in social networks (Sack, 200).</Paragraph>
      <Paragraph position="3"> 'Conversation Thumbnails' uses an overview/detail display to contextualize a user's post in the group as a whole while it is being composed (Wattenberg and Milen, 203). 'PeopleGarden' represents each individual participant as a composite of their history of posting. Having all participants represented in the same screen provides insight into the dynamics of the group as a whole acros its recorded history, although there is no way to track conections between individuals or threading (Xiong and Donath, 199).</Paragraph>
      <Paragraph position="4"> In RACE, the topics of a multitude of conversations are explored by an analyst loking for both episodic and social information. Through an iterative filtering process, the analyst examines individual conversations. Like the newsgroup visualizations above, the goals are (in addition to a general desire to understand what is going on) to determine an individual's social role and dynamic of the group, but the concept of &amp;quot;conversations&amp;quot; is more granular. Whereas the newsgroup visualizations may represent hundreds or even thousands of users and conversation threads, the detailed visualization in RACE's final screen represents a single discourse with as few as two people. Thus, the systems above deal with a higher level of abstraction and do not convey information on &amp;quot;lurkers&amp;quot; who may read but not post, emotional qualities of contributions, or the temporal information present in synchronous communication. RACE has the additional goals of denoting presence, affect, and what Viegas and Donath call &amp;quot;negotiation of conversational synchrony&amp;quot; (199).</Paragraph>
      <Paragraph position="5"> Research on chat room conversation has produced some interesting visualizations that start to deal with these concepts. The 'Babble' system both facilitates and visualizes synchronous and asynchronous chat (Erickson and Laff, 201). Users are represented as colored dots on a social proxy called a 'cookie'. The more interactions they have with the system, whether posting or only reading, the more central they become in the visualization. With inactivity, the dots move slowly back out to the periphery of the cookie, conveying information about presence and activity level.</Paragraph>
      <Paragraph position="6"> 'Chat Circles' is designed for synchronous chat and creates a strong sense of location by situating participants (represented as colored circles) in a large 2D space and only allowing them to see the text posted by others positioned nearby (Viegas and Donath, 199). The circles expand to encompass posted text and shrink when ample time to  read the uterance has passed. Even people who are idling or only listening are represented spatially so others can see them. People can position their circles to avoid the 'noise' of unrelated conversations (as one could do at a cocktail party) or signify whom they are addressing. Each post leaves a cumulative translucent trace, indicating how long the poster has been there and how active they have been. Thus, group dynamics such as a group conversation fragmenting into smaller ones, relative verbosity, and relative position are available for interpretation.</Paragraph>
      <Paragraph position="7"> While each of the systems above is designed for a particular modality, RACE integrates email, instant messaging, text messaging, phone conversations and teleconferences, in-person meetings in addition to chat or newsgroup participation. The goal is to get a more holistic sense of an individual throughout their discrete conversations and communication methods. As a post-hoc analysis tol, RACE aids the analyst by adding system interpretations of affect and social dynamics to the information represented in the prior art. It should be noted that this effort violates one of Erickson's six claims about social visualization: &amp;quot;Portray actions, not interpretation... users understand the context better than the system ever wil&amp;quot; (203). We agree in theory, but the needs of our analysts differ from those of a contributor to the conversation. Content-driven interpretations of group dynamics, affect, and social role complement ful-text transcripts of the conversations, providing shortcuts to insight.</Paragraph>
      <Paragraph position="8"> Below we discus further the requirements of our user group.</Paragraph>
    </Section>
    <Section position="2" start_page="63" end_page="64" type="sub_section">
      <SectionTitle>
2.2 Requirements Elicitation
</SectionTitle>
      <Paragraph position="0"> To ensure our research was applicable to our organization's misions and fulfiled the requirements and expectations of our user group, we enlisted the help of four analysts to determine specific requirements. These were to be our subject matter experts (SME's). Through interactions with our SME's we determined that while it is important to being able to understand a single conversation in time, it is just as, if not more, important to be able to comprehend the stream of conversations that occurs over longer periods, related to the same topic. For example, it is important to be able to intercept, process, and analyze a discusion between two individuals talking about making a homemade bomb, but it is even more important to place such a discusion within the context of the set of comunications leading to an understanding of the overarching plot. Such review can provide additional information that could be invaluable to the analyst. Other requirements identified as part of these sesions included: * The system should allow the analyst to get back to original source documents and be able to review the provenance.</Paragraph>
      <Paragraph position="1"> * The system should allow the analyst to annotate the communication events.</Paragraph>
      <Paragraph position="2"> * Consider the use of color for note taking and marking modalities.</Paragraph>
      <Paragraph position="3"> * The system should allow the analyst to highlight conversation fragments (i.e., small parts of a larger conversation that are considered important).</Paragraph>
      <Paragraph position="4"> * The system should provide basic translation mechanisms for foreign language suport as well as provide some form of lexicon for terms that fall outside an analyst's field of expertise. * The system should be able to import and export conversation fragments using common formats. The system should allow multiple analysts to work colaboratively within the same workspace.</Paragraph>
      <Paragraph position="5"> * The system should allow the analyst to customize the environment to their preferences.</Paragraph>
      <Paragraph position="6"> In addition to an informal list of requirements, a great deal of brainstorming was performed during this session. Folowing a participatory design process, system designers worked with SME's to put together a work process and some initial sketches of the overall system that could be fed into the implementation stage.</Paragraph>
      <Paragraph position="7"> The process was designed so that the analyst could (Figure 1) interact with the conversation corpus available to them (potentially produced as a result of a search), viewing the conversations as dots, clustered around major topics. This view could be filtered based on time period, participants involved and communications modality used.</Paragraph>
      <Paragraph position="8">  On selecting a subset of conversations to review further (Figure 2) the analyst moves through to a second screen (the sequence view) where they can analyze the conversations in relation to when they occurred (the view is reminiscent</Paragraph>
    </Section>
    <Section position="3" start_page="64" end_page="64" type="sub_section">
      <SectionTitle>
of Microsoft Project's Gant view).
</SectionTitle>
      <Paragraph position="0"> While icons and text wil continue to depict the modality the conversation utilized, the focus at this level is of fusing the conversations to build a sequenced stream of communications traffic so the underlying thread or purpose can be understod. Finally (Figure 3), conversations of specific interest to the analyst can be pursued in further detail in a third screen, called the 'detail view'. Here, the ful transcript is displayed and can be 'played' uterance by uterance in real time. As each uterance is reached, a text-to-speech engine speaks the words, while a number of visual representations indicate social constructs such as social roles and the dynamics between the individuals.</Paragraph>
    </Section>
    <Section position="4" start_page="64" end_page="66" type="sub_section">
      <SectionTitle>
2.3 Implemented Prototype
</SectionTitle>
      <Paragraph position="0"> Using a participatory design process, informed by the sketches and requirements of our analysts and the limitations of current research systems, we implemented a three-screen prototype analytical environment that allows a user to visualize a large corpus of communications events  The environment can run on three screens simultaneously, be split acros three panes (useful for performing analysis on large displays like wallmounted plasma displays) or on a single screen with the use of a window manager seen in the top right of each view.</Paragraph>
      <Paragraph position="1"> For the 'corpus view' (left hand screen, Figure 5) we customized some commercially available visualization software to present the conversation corpus, clustered by topic. Zoming in to individual items brings up metadata about that specific conversation. The different modalities may also be represented by different icons or colors,  depending on the type of style sheet loaded. Filters currently available include the modality used, the participants involved and the time/date the conversation occurred (and shortcuts to selecting all or none, or the current inverse are also available). Finally, a navigation window ensures the user does not get lost when interacting with a massive data that is topically diverse.</Paragraph>
      <Paragraph position="2">  The 'sequence view' (Figure 6) is where we envision the majority of an analyst's time wil be spent. It is here that they wil review, in detail, a small subset of conversations that they found of interest in the corpus space. For example, in their exploration of the visualization, the analyst may find a group of discusions about a particular chemical substance. Knowing that this is relevant to a study they are performing, they simply drag a box around that subset and immediately those conversations are shown in the sequence view. Each conversation has an independent time line and can be zoomed out to show the entire conversation or zoomed in to see the individual uterances (these may also be accessed using tol-tips). The conversation titles on the left hand side of the screen can be unexpanded to show all the participants involved. Clicking on the participant opens a dialog box containing known information about that individual (including any known aliases and other names they may use online). A global timeline at the botom of the screen shows where each conver- null Once an important conversation is uncovered through the triage process, it can be selected for deeper investigation in the details view (Figure 7). This view can enable the analyst to see beyond the individual uterances. Utilizing other research performed at the Pacific Northwest National Laboratory, the details view enables the analyst to gain insight into an individual's opinion on the topics discused. The transcript is color-coded to show the seven dimensions of affect (expression, power, ethics, attainment, skil, accomplishment and transactions), while a graph representation alows the analyst to compare individuals' affect against each other. In order to ingest the text in different ways, a 'text-to-speech' engine can be used to have the computer 'speak' the transcript. As it steps through the uterances, a group dynamics graphic (based on Erickson's Social Proxy) shows how the individuals relate to each other, highlighting those involved in the conversation and those that are idle. This view also provides a hierarchical view of the topics discused with the ability to triger a multi-dimensional visualization that maps participants to topics.</Paragraph>
    </Section>
  </Section>
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