Enhancing the Identification of Cyberbullying through Participant Roles
G.G. Udaya Priyasantha Rathnayake, Thushari Atapattu, Mahen Herath, Georgia Zhang, Katrina E. Falkner · 2020
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.