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Enhancing the Identification of Cyberbullying through Participant Roles by Ratnayaka, Gathika; Atapattu, Thushari; Herath, Mahen; Zhang, Georgia; Falkner, Katrina is a scholarly article available to read on EtoBox.

What is Enhancing the Identification of Cyberbullying through Participant Roles about?

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.

Author

Ratnayaka, Gathika; Atapattu, Thushari; Herath, Mahen; Zhang, Georgia; Falkner, Katrina

Published

2020

Language

EN