  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.
