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        <title>A Machine Learning Framework for Detecting Hate Speech and Fake Narratives in Hindi-English Tweets</title>
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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)</title>
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            <namePart type="given">Shankar</namePart>
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    <abstract>This paper presents a novel system developed for the Faux-Hate Shared Task at ICON2024, addressing the detection of hate speechand fake narratives within Hindi-English code-mixed social media data. Our approach com-bines advanced text preprocessing, TF-IDFvectorization, and Random Forest classifiersto identify harmful content, while employingSMOTE to address class imbalance. By lever-aging ensemble learning and feature engineer-ing, our system demonstrates robust perfor-mance in detecting hateful and fake content,classifying targets, and evaluating the sever-ity of hate speech. The results underscore thepotential for real-world applications, such asmoderating online platforms and identifyingharmful narratives. Furthermore, we highlightethical considerations for deploying such tools,emphasizing responsible use in sensitive do-mains, thereby advancing research in multilin-gual hate speech detection and online abusemitigation.</abstract>
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%0 Conference Proceedings
%T A Machine Learning Framework for Detecting Hate Speech and Fake Narratives in Hindi-English Tweets
%A Yadawad, R.n.
%A Saumya, Sunil
%A Nivedh, K.n.
%A Padanur, Siddhaling S.
%A Basti, Sudev
%Y Biradar, Shankar
%Y Reddy, Kasu Sai Kartheek
%Y Saumya, Sunil
%Y Akhtar, Md. Shad
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F yadawad-etal-2024-machine
%X This paper presents a novel system developed for the Faux-Hate Shared Task at ICON2024, addressing the detection of hate speechand fake narratives within Hindi-English code-mixed social media data. Our approach com-bines advanced text preprocessing, TF-IDFvectorization, and Random Forest classifiersto identify harmful content, while employingSMOTE to address class imbalance. By lever-aging ensemble learning and feature engineer-ing, our system demonstrates robust perfor-mance in detecting hateful and fake content,classifying targets, and evaluating the sever-ity of hate speech. The results underscore thepotential for real-world applications, such asmoderating online platforms and identifyingharmful narratives. Furthermore, we highlightethical considerations for deploying such tools,emphasizing responsible use in sensitive do-mains, thereby advancing research in multilin-gual hate speech detection and online abusemitigation.
%U https://aclanthology.org/2024.icon-fauxhate.8/
%P 40-44

Markdown (Informal)

[A Machine Learning Framework for Detecting Hate Speech and Fake Narratives in Hindi-English Tweets](https://aclanthology.org/2024.icon-fauxhate.8/) (Yadawad et al., ICON 2024)

• A Machine Learning Framework for Detecting Hate Speech and Fake Narratives in Hindi-English Tweets (Yadawad et al., ICON 2024)

ACL

• R.n. Yadawad, Sunil Saumya, K.n. Nivedh, Siddhaling S. Padanur, and Sudev Basti. 2024. A Machine Learning Framework for Detecting Hate Speech and Fake Narratives in Hindi-English Tweets. In Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate), pages 40–44, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).