Enhancing the Identification of Cyberbullying through Participant Roles

Gathika Rathnayake, Thushari Atapattu, Mahen Herath, Georgia Zhang, Katrina Falkner


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.
Anthology ID:
2020.alw-1.11
Volume:
Proceedings of the Fourth Workshop on Online Abuse and Harms
Month:
November
Year:
2020
Address:
Online
Venues:
ALW | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
89–94
Language:
URL:
https://aclanthology.org/2020.alw-1.11
DOI:
10.18653/v1/2020.alw-1.11
Bibkey:
Cite (ACL):
Gathika Rathnayake, Thushari Atapattu, Mahen Herath, Georgia Zhang, and Katrina Falkner. 2020. Enhancing the Identification of Cyberbullying through Participant Roles. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 89–94, Online. Association for Computational Linguistics.
Cite (Informal):
Enhancing the Identification of Cyberbullying through Participant Roles (Rathnayake et al., ALW 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.alw-1.11.pdf
Video:
 https://slideslive.com/38939535