CRII: CHS: Cyberbullying Detection Using Content and Social Network Analysis

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This project aims to define new approaches for automatic detection of cyberbullying by integrating the relevant research in social sciences and computer science. Cyberbullying is a critical social problem that occurs over a technical substrate. According to a recent National Crime Prevention Council report, more than 40% of teenagers in the US have reported being cyberbullied. This is especially worrying as the multiple studies have reported that the victims of cyberbullying often deal with psychiatric and psychosomatic disorders. Specifically, this research will advance the state of the art in cyberbullying detection beyond textual analysis by also giving due attention to the social relationships in which these bullying messages are exchanged. A higher accuracy at detection would allow for better mitigation of the cyberbullying phenomenon and may help improve the lives of thousands of victims who are cyberbullied each year. The results of this research will also open doors to employing social intervention mechanisms to help prevent cyberbullying incidents in future. The findings from this research will also validate and refine existing theories on cyberbullying and potentially advance the field by creating a wave of data-driven analysis of the phenomenon. The generated data set will be made available to the larger research community, thus enabling new findings that can help counter this social problem. This research will define new approaches for automatic detection of cyberbullying and validate and refine social science theories related to cyberbullying. To understand cyberbullying, experts in social science have focused on personality, social relationships, and psychological factors involving both the bully and the victim. Recently computer science researchers have also developed automated methods to identify cyberbullying messages by text mining cyber conversations. However, focusing only on the textual content may lead to a piecemeal understanding of the phenomenon and a limited detection performance. Hence, this research investigates: (1) whether analyzing social network features surrounding the network can improve the accuracy of cyberbullying detection, and (2) whether the findings of social science research on cyberbullying obtained via surveys, ethnography, and interview-based methods hold true when tested via automated data analysis undertaken at a much bigger scale. By analyzing the social relationship graph between users and deriving features such as number of friends, network embeddedness, and relationship centrality, the project will validate (and potentially refine) multiple theories in social science literature and assimilate those findings to create better cyberbullying detectors. The project will yield new, comprehensive models and algorithms that can be used for cyberbullying detection in automated settings.