Enhancing the Identification of Cyberbullying through Participant Roles

• URL: http://arxiv.org/abs/2010.06640v2
• Date: Fri, 23 Oct 2020 01:15:20 GMT
• Title: Enhancing the Identification of Cyberbullying through Participant Roles
• Authors: Gathika Ratnayaka, Thushari Atapattu, Mahen Herath, Georgia Zhang, Katrina Falkner
• Abstract summary: 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.
• Score: 1.399948157377307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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