Karthik Dinakar, Birago Jones, Catherine Havasi, Henry Lieberman, Rosalind W. Picard

Abstract

Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components – robust techniques for effective detection, and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like GoogleÊs GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art NLP and a Commonsense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with Bullying (e.g. appearance, intelligence racial and ethnic slurs, social acceptance and rejection), and construct BullySpace, a commonsense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with Commonsense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace Commonsense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy amongst social network participants. We propose an „air traffic control‰-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading, and to help them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.