LT4SG researchers presented their work on “Enhancing the detection of Cyberbullying through participant roles” in the Workshop on Online Abuse and Harm at EMNLP2020.
“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.”
Download the full paper from here – https://www.aclweb.org/anthology/2020.alw-1.11.pdf
Watch the video from here – https://slideslive.com/38939535/enhancing-the-identification-of-cyberbullying-through-participant-roles
#Cyberbullying #ParticipantRoles #EMNLP
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