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    <abstract>The exponential rise in social media users has revolutionized information accessibility and exchange. While these platforms serve various purposes, they also harbor negative elements, including hate speech and offensive behavior. Detecting hate speech in diverse languages has garnered significant attention in Natural Language Processing (NLP). This paper delves into hate speech detection in Tamil, particularly related to migration and refuge, contributing to the Caste/migration hate speech detection shared task. Employing a Convolutional Neural Network (CNN), our model achieved an F1 score of 0.76 in identifying hate speech and significant potential in the domain despite encountering complexities. We provide an overview of related research, methodology, and insights into the competition’s diverse performances, showcasing the landscape of hate speech detection nuances in the Tamil language.</abstract>
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%0 Conference Proceedings
%T Lidoma@LT-EDI 2024:Tamil Hate Speech Detection in Migration Discourse
%A Tash, M.
%A Ahani, Z.
%A Zamir, M.
%A Kolesnikova, O.
%A Sidorov, G.
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F tash-etal-2024-lidoma
%X The exponential rise in social media users has revolutionized information accessibility and exchange. While these platforms serve various purposes, they also harbor negative elements, including hate speech and offensive behavior. Detecting hate speech in diverse languages has garnered significant attention in Natural Language Processing (NLP). This paper delves into hate speech detection in Tamil, particularly related to migration and refuge, contributing to the Caste/migration hate speech detection shared task. Employing a Convolutional Neural Network (CNN), our model achieved an F1 score of 0.76 in identifying hate speech and significant potential in the domain despite encountering complexities. We provide an overview of related research, methodology, and insights into the competition’s diverse performances, showcasing the landscape of hate speech detection nuances in the Tamil language.
%U https://aclanthology.org/2024.ltedi-1.20
%P 184-189

Markdown (Informal)

[Lidoma@LT-EDI 2024:Tamil Hate Speech Detection in Migration Discourse](https://aclanthology.org/2024.ltedi-1.20) (Tash et al., LTEDI-WS 2024)

• Lidoma@LT-EDI 2024:Tamil Hate Speech Detection in Migration Discourse (Tash et al., LTEDI-WS 2024)

ACL

• M. Tash, Z. Ahani, M. Zamir, O. Kolesnikova, and G. Sidorov. 2024. Lidoma@LT-EDI 2024:Tamil Hate Speech Detection in Migration Discourse. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 184–189, St. Julian's, Malta. Association for Computational Linguistics.