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Cyberbullying is a pervasive problem in online communities. To identifycyberbullying cases in large-scale social networks, content moderators dependon machine learning classifiers for automatic cyberbullying detection. However,existing models remain unfit for real-world applications, largely due to ashortage of publicly available training data and a lack of standard criteriafor assigning ground truth labels. In this study, we address the need forreliable data using an original annotation framework. Inspired by socialsciences research into bullying behavior, we characterize the nuanced problemof cyberbullying using five explicit factors to represent its social andlinguistic aspects. We model this behavior using social network andlanguage-based features, which improve classifier performance. These resultsdemonstrate the importance of representing and modeling cyberbullying as asocial phenomenon.

Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification