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Providing Social Transparency through Visualizations in Wikipedia Ed H. Chi, Bongwon Suh, Aniket Kittur Palo Alto Research Center 3333 Coyote Hill Road, Palo Alto, CA 94304 USA {echi, suh, nkittur}@parc.com ABSTRACT
article, in a digestible form could affect users’ trust and interpretation of an article. If so, the approach can result in reducing the risks many perceive as inherent to a system [Denning05] in which anyone can contribute or change anything.
The purpose of social visual analytics is to enable a group of people to reason around data, enabling discovery and illumination of issues and further discussion, elaboration, and analysis. In this paper, we briefly describe a research project in which we released a tool called WikiDashboard that visualizes the social dynamics and editing patterns of every article and editor of Wikipedia. The idea is that social transparency might be able to provide the necessary cues for users of Wikipedia to have a constructive conversation about the editing patterns.
To address this challenge, we designed WikiDashboard (http://wikidashboard.parc.com), a tool that helps users to identify interesting edit patterns in Wikipedia pages, patterns that may be very hard to detect otherwise. RELATED WORK
Wikipedia is sometimes viewed with skepticism due to its mutable nature and user-generated content, leading to a number of high profile controversies surrounding its trustworthiness. The development of the WikiScanner tool (wikiscanner.virgil.gr) linking IP addresses with editing activity has also resulted in a number of allegations of individuals or organizations changing content in biased ways. For example, members of the U.S. Congress have been accused of making partisan changes to their own or others’ entries1. This distrust of Wikipedia is somewhat at odds with its remarkable popularity.
INTRODUCTION
Social data analysis is the idea that analytics should and does happen in a social context. What this means include features that enable (1) shared data or artifact; (2) shared workspaces; (3) shared intermediate results; and (4) shared annotations and comments. Systems such as ManyEyes and Swivel provide social workspaces for users to analyze data and to have a conversation around the data. Our interest is in similar veins, of enabling users to have ability to understand and have conversations around visual analysis of social patterns in Wikipedia.
We believe that the lack of trust in wiki-based and other mutable knowledge systems may lie not in the nature of the system itself, but rather in the lack of information available to the user in judging the trustworthiness of the content.
The open participatory model of wiki systems – allowing anyone change anything – makes them inherently dynamic and encourages them to have a large number of editors with various points of view. Furthermore, although users can access past revisions of every page, it is difficult and timeconsuming even for dedicated users to make sense of the history of a page, because many page histories run into the thousands of edits (as of June 16th of 2007, 10,453 Wikipedia pages had more than 1000 edits).
Disclosing patterns of past performance and providing rich feedback about users and content are best practices for increasing trust online [Shneiderman00]. These data can provide a meaningful history to users and help them judge the trustworthiness of other users or of information [Resnick00].
Our goal here is to investigate how providing access to this type of accountability information through a social visualization, i.e. who edits how many revisions for an
Our research is motivated by the challenge of helping users making sense of social history information in order to better judge the trustworthiness of mutable and user-generated content. Here, we are clearly inspired by the work of Viegas et al. in HistoryFlow [Viegas04], Conversation Map by Warren Sack [Sack00], and Answer Garden by Mark Ackerman [Ackerman]. These systems enabled a kind of social data analysis on the conversations that are being
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Figure 1 Article Dashboard. The top summary graph shows the weekly edit trend of this page. Below the summary graph, there is a list of active editors and their activities on the article. In this example, it is easy to identify that user Zz414 is the most active editor of the article. It is interesting to see that Zz414 suddenly stopped editing as of Feb. 2007.
carried out in collaborative knowledge spaces. Indeed, in a piece that inspired our work, Erickson et al. [Erickson02] discussed the importance of making socially-relevant hidden information visible in interactive communities. WIKIDASHBOARD
As shown in Figure 1, the site provides a dashboard for each page in Wikipedia, while proxying the rest of the content from Wikipedia. The dashboard provides a visualization overlay onto every live Wikipedia page, enabling users to be aware of social dynamics and context around the page they are about to read. The prototype can be used just as if users are on the Wikipedia site itself. All of the functions (such as the article search function, and the discuss and history tabs) work just as in Wikipedia. Based on the type of Wikipedia pages, the prototype provides two different types of dashboards: Article Dashboard and User Dashboard. Article Dashboard
Each article has an associated article dashboard that displays an aggregate edit activity graph representing the weekly edit trend of the article, followed by a list of the active users who made edits on the page (Figure 1 and Figure 2). The top summary graph shows two trends: a gray line graph representing the edits made on the article and a blue bar graph denoting the edits on the corresponding “Talk” discussion page. This graph can help users to easily identify any interesting incidents in the article history, e.g., a sudden burst of edits that might correspond with some heavy discussion. Below the article edit summary, the active users of the article are ordered by the number of edits they made on the page and its talk page combined, allowing users to easily identify who the major editors are for the page.
Furthermore, the weekly edit activity graph of each editor is also provided on the right side of the dashboard, enabling users to investigate when the edits by that editor were made. With this article-editor activity graph, where a darker red color denotes more activity, the tool can help users to examine patterns on how an article evolves over time between multiple editors, e.g., co-editing relationship, recent editors, the burstiness of edits, etc. User Dashboard
A user page is a special space like a home page to display information relating to a user. In our system, each user page has a User Dashboard embedded, displaying the article contribution and editing patterns of that user (Figure 3). Similar to the Article Dashboard, the top summary graph shows the editor’s weekly edit activity (c.f. Article Dashboard summarizes edit activities made on each article), which allows users to easily examine the editor’s overall edit patterns in the past. The summary graph is followed by the list of Wikipedia pages where the editor has made edits. The list is ordered by the volume of contribution and includes the corresponding article-editor activity graphs on the right side.
Figure 2 User Dashboard is embedded in each user page of Wikipedia. The dashboard displays weekly edit trend of an editor as well as the list of articles that the editor made revisions on. This example shows a user, “Wasted Time R” made significant edits on articles related to New York politicians and pop singers.
As shown in Figure 3, User Dashboard is designed to facilitate an easy inspection of topics of interest that the editor might have. Furthermore, the dashboard allows investigation of the evolution of editor’s topics of interest. It is easy to identify that the editor in Figure 3 recently developed interest in the Rudy Giuliani article.
system; and supporting accountability for actions. The idea of social translucence suggests that WikiDashboard could benefit not only readers but also improve the effectiveness of active writers. Indeed, we are very interested in analyzing the impact of improved social transparency on both readers and editors. We plan to explore how readers process the given social context and use the information to evaluate user-generated contents as well as potential behavioral change of editors due to improved social transparency.
Article titles and user names in both dashboards are clickable links, allowing users to browse through them for further exploration. For example, clicking on an article title bring up the corresponding article and article dashboard.
REFERENCES
DISCUSSION
1. Ackerman, Mark S., and Thomas W. Malone. "Answer Garden: A Tool for Growing Organizational Memory." Proc. of the ACM Conference on Office Information Systems (COIS'90), April, 1990, pp. 31-39.
Clearly, our hope is to enable end-users to perform analytic of who has been editing each article, and follow those user trails to follow the editors’ interests. Importantly, the ability to perform pivot browsing between the two views enables quick analytics. In the visualizations of the user dashboards, end-users can get a sense of what the potential expertise are for editors on Wikipedia. In the article dashboards on the other hand, one can get a sense of what the top editors are for that page, and the relative stability of the article. Moreover, at a glance, end-users can assess the time length of interest of a particular top editor for a particular article topic.
2. Denning, P., Horning, J., Parnas, D., and Weinstein, L. “Wikipedia risks”, Comm. ACM, 48(12), ACM Press (2005), 152-152. 3. Erickson, T., Halverson, C., Kellogg, W.A., Laff, M., and Wolf, T. 2002. "Social translucence: designing social infrastructures that make collective activity visible", Comm. ACM, 45 (4), 40-44. ACM Press. 4. Resnick, P., Kuwabara, K., Zeckhauser, R., and Friedman, E. 2000. Reputation systems. Communications of the ACM 43, 45-48.
We have had over 5,200 visits and 25,400 page views since the opening of the tool in early September 2007, and received nice feedback on the visualization. For example,
5. Sack, W. 2000. Conversation map: a content-based Usenet newsgroup browser. In Proc. of the 5th international Conference on intelligent User interfaces IUI '00. ACM, New York, NY, 233-240. DOI= http://doi.acm.org/10.1145/325737.325856
“This is very useful for getting a quick glance of the user's editing interests over time. … I actually think a tool like WikiDashboard presents significantly more utility, and is the beginning of an interesting trend of repurposing metadata to create a trust heuristic.” 2
6. Shneiderman, B. 2000. Designing trust into online experiences. Communications of the ACM 43, 57-59.
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Perhaps the most interesting is the discussion forum at the Wikipedia Review, a criticism site devoted to Wikipedia and its oversight. Some of the discussions there focused on a specific analysis of the user “SlimVirgin”, who has apparently been embroiled in a discussion of “New_antisemitism” and “Animal_rights”. These discussions point to the potential for even better social data analysis if a discussion tool was better integrated within WikiDashboard on each topic page. Our hope had been better uptake of WikiDashboard in the Wikipedian community, and that discussions of social patterns would occur directly within Wikipedia’s discussion system instead of having a separate discussion forum.
7. Viégas, F.B., Wattenberg M., Dave K., “Studying cooperation and conflict between authors with history flow visualizations”, In Proc. of CHI 2004, ACM Press (2004), 575-582.
In either case, theories of social translucence [Erickson02] state that three building blocks are necessary for effective communication and collaboration: making socially significant information visible and salient; supporting awareness of the rules and constraints governing the
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