CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection

Chakraborty, S and Dutta, P and Roychowdhury, S and Mukherjee, A (2022) CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection. In: 2022 Findings of the Association for Computational Linguistics: NAACL 2022, 10 - 15 July 2022, Seattle, pp. 1874-1886.

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Abstract

The last decade has witnessed a surge in the interaction of people through social networking platforms. While there are several positive aspects of these social platforms, their proliferation has led them to become the breeding ground for cyber-bullying and hate speech. Recent advances in NLP have often been used to mitigate the spread of such hateful content. Since the task of hate speech detection is usually applicable in the context of social networks, we introduce CRUSH, a framework for hate speech detection using User Anchored selfsupervision and contextual regularization. Our proposed approach secures ˜ 1-12% improvement in test set metrics over best performing previous approaches on two types of tasks and multiple popular English language social networking datasets.

Item Type:	Conference Paper
Publication:	Findings of the Association for Computational Linguistics: NAACL 2022 - Findings
Publisher:	Association for Computational Linguistics (ACL)
Additional Information:	The copyright for this article belongs to Association for Computational Linguistics (ACL).
Keywords:	Computational linguistics; Natural language processing systems, Breeding grounds; Cyber bullying; English languages; Regularisation; Social-networking; Speech detection; Test sets, Speech recognition
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation Others
Date Deposited:	05 Oct 2022 06:09
Last Modified:	19 May 2023 10:09
URI:	https://eprints.iisc.ac.in/id/eprint/77039

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