Using Text Similarity to Detect Social Interactions not Captured by Formal Reply Mechanisms

In modeling social interaction online, it is important to understand when people are reacting to each other. Many systems have explicit indicators of replies, such as threading in discussion forums or replies and retweets in Twitter. However, it is likely these explicit indicators capture only part of people's reactions to each other, thus, computational social science approaches that use them to infer relationships or influence are likely to miss the mark. This paper explores the problem of detecting non-explicit responses, presenting a new approach that uses tf-idf similarity between a user's own tweets and recent tweets by people they follow. Based on a month's worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion.Comment: A final version of this work was published in the 2015 IEEE 11th International Conference on e-Science (e-Science

Friends, Strangers, and the Value of Ego Networks for Recommendation

Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform no worse than those based on the full network, even though they require much less data and computational resources. Further, our evidence suggests that locality of preference, or the non-random distribution of item preferences in a social network, is a driving force behind the value of incorporating social network information into recommender algorithms. When locality is high, as in Twitter data, simple k-nn recommenders do better based only on friends than they do if they draw from the entire network. These results help us understand when, and why, social network information is likely to support recommendation systems, and show that systems that see ego-centric slices of a complete network (such as websites that use Facebook logins) or have computational limitations (such as mobile devices) may profitably use ego-centric recommendation algorithms.Comment: 5 pages, ICWSM 201

Self-monitoring Practices, Attitudes, and Needs of Individuals with Bipolar Disorder: Implications for the Design of Technologies to Manage Mental Health

Objective To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. Materials and Methods Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. Results Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. Discussion Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. Conclusion This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management

Rulemaking 2.0

In response to President Obama\u27s Memorandum on Transparency and Open Government, federal agencies are on the verge of a new generation in online rulemaking. However, unless we recognize the several barriers to making rulemaking a more broadly participatory process, and purposefully adapt Web 2.0 technologies and methods to lower those barriers, Rulemaking 2.0 is likely to disappoint agencies and open-government advocates alike. This article describes the design, operation, and initial results of Regulation Room, a pilot public rulemaking participation platform created by a cross-disciplinary group of Cornell researchers in collaboration with the Department of Transportation. Regulation Room uses selected live rulemakings to experiment with human and computer support for public comment. The ultimate project goal is to provide guidance on design, technological, and human intervention strategies, grounded in theory and tested in practice, for effective Rulemaking 2.0 systems. Early results give some cause for optimism about the open-government potential of Web 2.0-supported rulemaking. But significant challenges remain. Broader, better public participation is hampered by 1) ignorance of the rulemaking process; 2) unawareness that rulemakings of interest are going on; and 3) information overload from the length and complexity of rulemaking materials. No existing, commonly used Web services or applications are good analogies for what a Rulemaking 2.0 system must do to lower these barriers. To be effective, the system must not only provide the right mix of technology, content, and human assistance to support users in the unfamiliar environment of complex government policymaking; it must also spur them to revise their expectations about how they engage information on the Web and also, perhaps, about what is required for civic participation