Enhancing the Identification of Cyberbullying through Participant Roles

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

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arXiv · 2010.06640

Enhancing the Identification of Cyberbullying through Participant Roles

Gathika Ratnayaka·Thushari Atapattu·Mahen Herath·Georgia Zhang·Katrina Falkner

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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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BibTeX RIS

Gathika Ratnayaka, Thushari Atapattu, Mahen Herath, Georgia Zhang, Katrina Falkner. 2020-10-13. Enhancing the Identification of Cyberbullying through Participant Roles. https://arxiv.org/abs/2010.06640

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