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

  • Caleb Ziems Emory University
  • Ymir Vigfusson Emory University
  • Fred Morstatter USC Information Sciences Institute

Abstract

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.

Published
2020-05-26
How to Cite
Ziems, C., Vigfusson, Y., & Morstatter, F. (2020). Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 808-819. Retrieved from https://www.aaai.org/ojs/index.php/ICWSM/article/view/7345