Social motivation and point of view

Social media facilitates interaction and information dissemination among an unprecedented number of participants. Why do users contribute, and why do they contribute to a specific venue? Does the information they receive cover all relevant points of view, or is it biased? The substantial and increasing popularity of social media makes these questions more pressing, but also puts answers within reach of automated methods. I investigate scalable algorithms for finding user behavior changes, predicting the effect of feedback on where users will make contributions, and evaluating the topics and points of view presented in peer-produced content. Users tend to take actions which in the past have led to social interaction, creating herding effects when large groups exchange feedback. In peer production, positive and negative interactions between users can reveal topical disputes, enabling inferences about points of view. Such learning from large-scale social interactions allows us to monitor the quality of information and the health of venues, but also provides fresh insights into human behavior.