Enhancing the Identification of Cyberbullying through Participant Roles
This addresses the need for more nuanced cyberbullying detection for online safety, though it is incremental as it builds on existing detection methods.
The paper tackled the problem of cyberbullying detection by moving beyond binary classification to identify participant roles like victim and harasser, achieving F1-scores of 0.83 for cyberbullying and 0.76 for role classification.
Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.