Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one’s curiosity about a countless number of things. In this paper, we seek to understand YA’s knowledge sharing activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users ’ interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics

Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create communitymaintained artifacts of lasting value (CALV) that support the group’s main purpose and provide value to others. Said communities don’t help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more. We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot’s intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.

People come to online communities seeking information, encouragement, and conversation. When a community responds, participants benefit and become more committed. Yet interactions often fail. In a longitudinal sample of 6,172 messages from 8 Usenet newsgroups, 27 % of posts received no response. The information context, posters ’ prior engagement in the community, and the content of their posts all influenced the likelihood that they received a reply, and, as a result, their willingness to continue active participation. Posters were less likely to get a reply if they were newcomers. Posting on-topic, introducing oneself via autobiographical testimonials, asking questions, using less complex language and other features of the messages, increased replies. Results suggest ways that developers might increase the ability of online communities to support successful individual-group interactions. Author Keywords Online communities, community success, contribution,

Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution. In particular, we find support for social learning: newcomers who see their friends contributing go on to share more content themselves. For newcomers who are initially inclined to contribute, receiving feedback and having a wide audience are also predictors of increased sharing. On the other hand, singling out appears to affect only those newcomers who are not initially inclined to share. The paper concludes with design implications for motivating newcomer sharing in online communities. Author Keywords Social network sites, SNS, online communities, motivating

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The social news aggregator Digg allows users to submit and moderate stories by voting on (digging) them. As is true of most social sites, user participation on Digg is nonuniformly distributed, with few users contributing a disproportionate fraction of content. We studied user participation on Digg, to see whether it is motivated by competition, fueled by user ranking, or social factors, such as community acceptance. For our study we collected activity data of the top users weekly over the course of a year. We computed the number of stories users submitted, dugg or commented on weekly. We report a spike in user activity in September 2006, followed by a gradual decline, which seems unaffected by the elimination of user ranking. The spike can be explained by a controversy that broke out at the beginning of September 2006. We believe that the lasting acrimony that this incident has created led to a decline of top user participation on Digg. 1

We present two studies of online ephemerality and anonymity based on the popular discussion board /b/ at 4chan.org: a website with over 7 million users that plays an influential role in Internet culture. Although researchers and practitioners often assume that user identity and data permanence are central tools in the design of online communities, we explore how /b / succeeds despite being almost entirely anonymous and extremely ephemeral. We begin by describing /b / and performing a content analysis that suggests the community is dominated by playful exchanges of images and links. Our first study uses a large dataset of more than five million posts to quantify ephemerality in /b/. We find that most threads spend just five seconds on the first page and less than five minutes on the site before expiring. Our second study is an analysis of identity signals on 4chan, finding that over 90 % of posts are made by fully anonymous users, with other identity signals adopted and discarded at will. We describe alternative mechanisms that /b / participants use to establish status and frame their interactions.

Little is known about the impact of politeness in online communities. We use an inductive approach to automatically model linguistic politeness in online discussion groups and determine the impact of politeness on desired outcomes, such as increased reply rates. We describe differences in perceived politeness across a variety of groups and find that, controlling for group norms of responsiveness and message length, politeness increases reply rates in some technical groups, but rudeness is more effective in some political groups. The perceived politeness scores will be used to validate linguistic politeness strategies from theory and to inform the creation of a machine learning model of linguistic politeness that can be applied as a “politeness checker ” to educate newcomers to write in ways likely to elicit response from specific communities or as a rudeness detection tool for moderators. Author Keywords Linguistic politeness, computer-mediated communication,

In this position paper, we proposed a frame work of studying user acceptance of internal microblogging at work by discussing related theories and describing our data collection. We also reported several initial finding highlights. ACM Classification

Cyberspace has introduced new and intriguing means for knowledge sharing as well as new structures of mediated knowledge-building communities. Considering the various forms of online communities, it should be difficult to overstate the significance of Wikipedia as a landmark in building communal knowledge repositories. Wikipedia is an online collaboratively written encyclopedia. It has unique aspects of users ’ involvement in the production of content and its function as a community. In less than five years of existence, Wikipedia has acquired both avid advocates and ardent adversaries. Although there have been some public and academic debates about the quality of its content, as the rapid growth of its articles and numbers of active users (Wikipedians) continues, most people agree that at least the English version of Wikipedia is approaching critical mass where substantial content disasters should become rare. Wikipedia’s existence and success rely on users ’ inputs. Our chapter focuses on Wikipedians ’ incentives for contributing to Wikipedia. The popular observation is that Wikipedia only works in practice. In theory, it can never work. How does Wikipedia mobilize the levels of participation that make it “work in practice”? Wikipedia’s growth, from the time of its foundation in 2001, has been impressive in all conceivable dimensions. Expansion metrics have accelerated in terms of volume, numbers of articles, visitors, and percentage of contributors. There are, by the time of this writing, 250 language editions of Wikipedia. The English-language version is the largest. It contains more than two million articles. The German-language version has more than half a million articles