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.
@article{arxiv.2010.06640,
title = {Enhancing the Identification of Cyberbullying through Participant Roles},
author = {Gathika Ratnayaka and Thushari Atapattu and Mahen Herath and Georgia Zhang and Katrina Falkner},
journal= {arXiv preprint arXiv:2010.06640},
year = {2020}
}