Wikipedia is playing an increasingly central role on the web, and the policies its contributors follow when sourcing and fact-checking content affect million of readers. Among these core guiding principles, verifiability policies have a particularly important role. Verifiability requires that information included in a Wikipedia article be corroborated against reliable secondary sources. Because of the manual labor needed to curate Wikipedia at scale, however, its contents do not always evenly comply with these policies. Citations (i.e. reference to external sources) may not conform to verifiability requirements or may be missing altogether, potentially weakening the reliability of specific topic areas of the free encyclopedia. In this paper, we aim to provide an empirical characterization of the reasons why and how Wikipedia cites external sources to comply with its own verifiability guidelines. First, we construct a taxonomy of reasons why inline citations are required, by collecting labeled data from editors of multiple Wikipedia language editions. We then crowdsource a large-scale dataset of Wikipedia sentences annotated with categories derived from this taxonomy. Finally, we design algorithmic models to determine if a statement requires a citation, and to predict the citation reason . We evaluate the accuracy of such models across different classes of Wikipedia articles of varying quality, and on external datasets of claims annotated for fact-checking purposes.

Effective socialization of new contributors is vital for the long-term sustainability of open collaboration projects. Previous research has identified many common barriers to participation. However, few interventions employed to increase newcomer retention over the long term by improving aspects of the onboarding experience have demonstrated success. This study presents an evaluation of the impact of one such intervention, the Wikipedia Teahouse, on new editor survival. In a controlled experiment, we find that new editors invited to the Teahouse are retained at a higher rate than editors who do not receive an invite. The effect is observed for both low-and high-activity newcomers, and for both short- and long-term survival.

Donation-based support for open, peer production projects such as Wikipedia is an important mechanism for preserving their integrity and independence. For this reason understanding donation behavior and incentives is crucial in this context. In this work, using a dataset of aggregated donation information from Wikimedia's 2015 fund-raising campaign, representing nearly 1 million pages from English and French language versions of Wikipedia, we explore the relationship between the properties of contents of a page and the number of donations on this page. Our results suggest the existence of a reciprocity mechanism, meaning that articles that provide more utility value attract a higher rate of donation. We discuss these and other findings focusing on the impact they may have on the design of banner-based fundraising campaigns. Our findings shed more light on the mechanisms that lead people to donate to Wikipedia and the relation between properties of contents and donations.

Open online communities rely on social norms for behavior regulation, group cohesion, and sustainability. Research on the role of social norms online has mainly focused on one source of influence at a time, making it difficult to separate different normative influences and understand their interactions. In this study, we use the Focus Theory to examine interactions between several sources of normative influence in a Wikipedia sub-community: local descriptive norms, local injunctive norms, and norms imported from similar sub-communities. We find that exposure to injunctive norms has a stronger effect than descriptive norms, that the likelihood of performing a behavior is higher when both injunctive and descriptive norms are congruent, and that conflicting social norms may negatively impact pro-normative behavior. We contextualize these findings through member interviews, and discuss their implications for both future research on normative influence in online groups and the design of systems that support open collaboration.

Integrating new users into a community with complex norms presents a challenge for peer production projects like Wikipedia. We present The Wikipedia Adventure (TWA): an interactive tutorial that offers a structured and gamified introduction to Wikipedia. In addition to describing the design of the system, we present two empirical evaluations. First, we report on a survey of users, who responded very positively to the tutorial. Second, we report results from a large-scale invitation-based field experiment that tests whether using TWA increased newcomers' subsequent contributions to Wikipedia. We find no effect of either using the tutorial or of being invited to do so over a period of 180 days. We conclude that TWA produces a positive socialization experience for those who choose to use it, but that it does not alter patterns of newcomer activity. We reflect on the implications of these mixed results for the evaluation of similar social computing systems.

We report a study of Wikipedia in which we use a mixed-methods approach to understand how participation in specialized workgroups called WikiProjects has changed over the life of the encyclopedia. While previous work has analyzed the work of WikiProjects in supporting the development of articles within particular subject domains, the collaborative role of WikiProjects that do not fit this conventional mold has not been empirically examined. We combine content analysis, interviews and analysis of edit logs to identify and characterize these alternative WikiProjects and the work they do. Our findings suggest that WikiProject participation reflects community concerns and shifts in the community's conception of valued work over the past six years. We discuss implications for other open collaborations that need flexible, adaptable coordination mechanisms to support a range of content creation, curation and community maintenance tasks.

We present the Teahouse, a pilot project for supporting and socializing new Wikipedia editors. Open collaboration systems like Wikipedia must continually recruit and retain new members in order to sustain themselves. Wikipedia's editor decline presents unique exigency for evaluating novel strategies to support newcomers and increase new user retention in such systems, particularly among demographics that are currently underrepresented in the user community. In this paper, we describe the design and deployment of Teahouse, and present preliminary findings. Our findings highlight the importance of intervening early in the editor lifecycle, providing user-friendly tools, creating safe spaces for newcomers, and facilitating positive interactions between newcomers and established community members.

In online groups, increasing explicit coordination can increase group cohesion and member productivity. On Wikipedia, groups called WikiProjects employ a variety of explicit coordination mechanisms to motivate and structure member contribution, with the goal of creating and improving articles related to particular topics. However, while explicit coordination works well for coordinating article-level actions, coordinating group tasks and tracking progress towards group goals that involve tracking hundreds or thousands of articles over time requires different coordination strategies.

WikiProjects have contributed to Wikipedia's success in important ways, yet the range of work that WikiProjects perform and the way they coordinate that work remains largely unexplored. In this study, we perform a content analysis of 788 work-related discussions from the talk pages of 138 WikiProjects in order to understand the role WikiProjects play in collaborative work on Wikipedia. We find that the editors use WikiProjects to coordinate a wide variety of work activities beyond content production and that non-members play an active role in that work. Our research suggests that WikiProject collaboration is less structured and more open than that of many virtual teams and that WikiProjects may function more like FLOSS projects than traditional groups.

A lack of support for active listening undermines discussion and deliberation on the web. We contribute a design frame identifying potential improvements to web discussion were listening more explicitly encouraged in interfaces. We explore these concepts through a novel interface, Reflect, that creates a space next to every comment where others can summarize the points they hear the commenter making. Deployments on Slashdot, Wikimedia's Strategic Planning Initiative, and a local civic effort suggest that interfaces for listening may have traction for general use on the web.

We present a novel platform for supporting public deliberation on difficult decisions. ConsiderIt guides people to reflect on tradeoffs and the perspectives of others by framing interactions around pro/con points that participants create, adopt, and share. ConsiderIt surfaces the most salient pros and cons overall, while also enabling users to drill down into the key points for different groups. We deployed ConsiderIt in a contentious U.S. state election, inviting residents to deliberate on nine ballot measures. We discuss ConsiderIt's affordances and limitations, enriched with empirical data from this deployment. We show that users often engaged in normatively desirable activities, such as crafting positions that recognize both pros and cons, as well as points written by people who do not agree with them.

This poster introduces Qbox, a flexible tool developed by the Communicative Practices in Virtual Workspaces research group at the University of Washington to support traditional and innovative forms of analysis for web-based and digital material. Qbox integrates three functional areas of work associated with content analysis: consolidating and presenting source data, performing coding or classification work, and analyzing results. Developed using an iterative user-centered design approach to support ongoing research, this tool enhances research protocol by providing a flexible application to organize digital spaces, and demonstrates the power of productivity associated with agile, user-based development.

Groups collaborating in online spaces on complex, extended projects develop behavioral conventions and agreed-upon practices to structure and regulate their interactions and work. Collaborators on Wikipedia have developed a multi-tiered policy environment to document a set of evolving principles, processes, and rules to facilitate productive group collaboration. Previous quantitative studies have noted this hierarchical structure, but have evaluated the policy environment as a singular entity rather than investigating potential differences between the three main regulatory genres that enable it. These studies also excluded essays, the least official regulatory genre, from their analyses. We perform a comparative content analysis of all three genres (policies, guidelines, and essays) and demonstrate that they focus on different areas of community regulation. Drawing on the theory of genre ecologies we discuss the possible role of unofficial genres such as essays in articulating and regulating work practices in online, organized collaborative work.

This video is an example of an engineering outreach activity that we designed to illustrate some of the core issues in HRI research. High school students attending the University of Washington 2009 Summer Math Academy were given a disaster scenario and were asked think about how a robot could help a victim who was trapped by fallen rubble during an earthquake. Students were led through a series of thought questions that encouraged them to consider the type of information the robot would need to give to the victim, the victim's family, and the rescue team. They also considered how a victim might respond to a robot, and the behaviors the robot should display during the rescue. The students then created a script for the rescue scenario, writing not only their own lines, but the behavior and communication of the robot as well. All relevant consent forms were obtained from the participants prior to the outreach event.

This paper looks at the problem of detecting a particular type of social behavior in discussions: attempts to establish credibility as an authority on a particular topic. Using maximum entropy modeling, we explore questions related to feature extraction and turn vs. discussion-level modeling in experiments with online discussion text given only a small amount of labeled training data. We also introduce a method for learning interaction words from unlabeled data. Preliminary experiments show that a word-based approach (as used in topic classification) can be used successfully for turn-level modeling, but is less effective at the discussion level. We also find that sentence complexity features are almost as useful as lexical features, and that interaction words are more robust than the full vocabulary when combined with other features.