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