Jilin Chen

Short and tweet: experiments on recommending content from information streams

More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72% from a baseline of 33%. We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams.

Creating, destroying, and restoring value in wikipedia

Wikipedias brilliance and curse is that any user can edit any of the encyclopedia entries. We introduce the notion of the impact of an edit, measured by the number of times the edited version is viewed. Using several datasets, including recent logs of all article views, we show that an overwhelming majority of the viewed words were written by frequent editors and that this majority is increasing. Similarly, using the same impact measure, we show that the probability of a typical article view being damaged is small but increasing, and we present empirically grounded classes of damage. Finally, we make policy recommendations for Wikipedia and other wikis in light of these findings.

Eddi: interactive topic-based browsing of social status streams

Twitter streams are on overload: active users receive hundreds of items per day, and existing interfaces force us to march through a chronologically-ordered morass to find tweets of interest. We present an approach to organizing a users own feed into coherently clustered trending topics for more directed exploration. Our Twitter client, called Eddi, groups tweets in a users feed into topics mentioned explicitly or implicitly, which users can then browse for items of interest. To implement this topic clustering, we have developed a novel algorithm for discovering topics in short status updates powered by linguistic syntactic transformation and callouts to a search engine. An algorithm evaluation reveals that search engine callouts outperform other approaches when they employ simple syntactic transformation and backoff strategies. Active Twitter users evaluated Eddi and found it to be a more efficient and enjoyable way to browse an overwhelming status update feed than the standard chronological interface.

Conference paper selectivity and impact

Conference acceptance rate signals future impact of published conference papers.

Searching for the goldilocks zone: trade-offs in managing online volunteer groups

Dedicated and productive members who actively contribute to community efforts are crucial to the success of online volunteer groups such as Wikipedia. What predicts member productivity? Do productive members stay longer? How does involvement in multiple projects affect member contribution to the community? In this paper, we analyze data from 648 WikiProjects to address these questions. Our results reveal two critical trade-offs in managing online volunteer groups. First, factors that increase member productivity, measured by the number of edits on Wikipedia articles, also increase likelihood of withdrawal from contributing, perhaps due to feelings of mission accomplished or burnout. Second, individual membership in multiple projects has mixed effects. It decreases the amount of work editors contribute to both the individual projects and Wikipedia as a whole. It increases withdrawal for each individual project yet reduces withdrawal from Wikipedia. We discuss how our findings expand existing theories to fit the online context and inform the design of new tools to improve online volunteer work.

Techlens: a researcher's desktop

Rapid and continuous growth of digital libraries, coupled with brisk advancements in technology, has driven users to seek tools and services that are not only customized to their specific needs, but are also helpful in keeping them stay abreast with the latest developments in their field. TechLens is a recommender system that learns about its users through implicit feedback, builds correlations among them, and uses that information to generate recommendations that match the users profile. It gives users control over which parts of their profile of known citations are used in forming recommendations for new articles. This demonstration is a prototype that showcases some of the tools and services that TechLens offers to the users of digital libraries.

The Impact and Evolution of Group Diversity in Online Open Collaboration

Online open collaboration efforts, such as Wikipedia articles and open source software development, often involve a large crowd with diverse experiences and interests. Diversity, on the one hand, facilitates the access to and integration of a wide variety of information; on the other hand, it may cause conflict and hurt group performance. Although diversity’s effects have been the subject of many studies in offline work groups (with the results remaining inconclusive), its effects in online self-organizing groups are underexplored. In this paper, we examine 648 WikiProjects to understand (1) how tenure disparity and interest variety affect group productivity and member withdrawal and (2) how the two types of diversity evolve over time. Our results show a curvilinear effect of tenure disparity, which increases productivity and decreases member withdrawal, up to a point. Beyond that point, productivity slightly decreases, and members are more likely to withdraw. In comparison, our results show a positive ef...