III: Small: Advancing the Scientific Understanding of Bullying Through the Lens of Social Media

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Bullying has been recognized as a serious national health issue. Traditional approaches to the scientific study of bullying are hindered by data acquisition. For example, the standard approach has been to conduct personal surveys in schools. Due to its relatively small sample size and low temporal resolution, neither the true frequency of bullying over the population nor the evolution of bullying roles can be satisfactorily studied. The traditional approaches are also very labor intensive. Social media has developed to the point where it contains enough signal about bullying. This project develops novel machine learning models that automatically monitor and analyze publicly available social media data to understand bullying. These machine learning models reconstruct hidden bullying episodes from a sequence of social media posts. They automatically determine who participated in which bullying episode as what role. In addition, this project conducts human studies on bullying in school and in social media in parallel, by collecting self-report surveys by school-aged children and their social media posts simultaneously. Such studies correlate the traditional psychological approach and social media data on bullying. Taken together, the project will provide significant new scientific data toward understanding, intervention, and helping policy-making regarding bullying.