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        <title>CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection</title>
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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 self-supervision 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.</abstract>
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%0 Conference Proceedings
%T CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection
%A Chakraborty, Souvic
%A Dutta, Parag
%A Roychowdhury, Sumegh
%A Mukherjee, Animesh
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F chakraborty-etal-2022-crush
%X 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 self-supervision 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.
%R 10.18653/v1/2022.findings-naacl.144
%U https://aclanthology.org/2022.findings-naacl.144
%U https://doi.org/10.18653/v1/2022.findings-naacl.144
%P 1874-1886

Markdown (Informal)

[CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection](https://aclanthology.org/2022.findings-naacl.144) (Chakraborty et al., Findings 2022)

• CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection (Chakraborty et al., Findings 2022)

ACL

• Souvic Chakraborty, Parag Dutta, Sumegh Roychowdhury, and Animesh Mukherjee. 2022. CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1874–1886, Seattle, United States. Association for Computational Linguistics.