@inproceedings{rathnayake-etal-2020-enhancing,
title = "Enhancing the Identification of Cyberbullying through Participant Roles",
author = "Rathnayake, Gathika and
Atapattu, Thushari and
Herath, Mahen and
Zhang, Georgia and
Falkner, Katrina",
editor = "Akiwowo, Seyi and
Vidgen, Bertie and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.alw-1.11",
doi = "10.18653/v1/2020.alw-1.11",
pages = "89--94",
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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<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.</abstract>
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%0 Conference Proceedings
%T Enhancing the Identification of Cyberbullying through Participant Roles
%A Rathnayake, Gathika
%A Atapattu, Thushari
%A Herath, Mahen
%A Zhang, Georgia
%A Falkner, Katrina
%Y Akiwowo, Seyi
%Y Vidgen, Bertie
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Fourth Workshop on Online Abuse and Harms
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F rathnayake-etal-2020-enhancing
%X 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.
%R 10.18653/v1/2020.alw-1.11
%U https://aclanthology.org/2020.alw-1.11
%U https://doi.org/10.18653/v1/2020.alw-1.11
%P 89-94
Markdown (Informal)
[Enhancing the Identification of Cyberbullying through Participant Roles](https://aclanthology.org/2020.alw-1.11) (Rathnayake et al., ALW 2020)
ACL