Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments

Millon Das, Punyajoy Saha, Mithun Das


Abstract
The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. Hence, few of the past works transform the same task into a regression. This paper shows the comparative evaluation of different transformers and traditional machine learning models on a recently released toxicity severity measurement dataset by Jigsaw. We further demonstrate the issues with the model predictions using explainability analysis.
Anthology ID:
2022.trac-1.2
Volume:
Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Ritesh Kumar, Atul Kr. Ojha, Marcos Zampieri, Shervin Malmasi, Daniel Kadar
Venue:
TRAC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–15
Language:
URL:
https://aclanthology.org/2022.trac-1.2
DOI:
Bibkey:
Cite (ACL):
Millon Das, Punyajoy Saha, and Mithun Das. 2022. Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments. In Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022), pages 10–15, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments (Das et al., TRAC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.trac-1.2.pdf
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