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        <title>Shared Feature-Based Multitask Model for Faux-Hate Classification in Code-Mixed Text</title>
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    <name type="personal">
        <namePart type="given">Sanjana</namePart>
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    <name type="personal">
        <namePart type="given">Rashmi</namePart>
        <namePart type="family">Rachh</namePart>
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    <name type="personal">
        <namePart type="given">Prakul</namePart>
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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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        <name type="personal">
            <namePart type="given">Shankar</namePart>
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            <namePart type="given">Kasu</namePart>
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            <namePart type="given">Kartheek</namePart>
            <namePart type="family">Reddy</namePart>
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            <namePart type="given">Shad</namePart>
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    <abstract>In recent years, the rise of harmful narratives online has highlighted the need for advancedhate speech detection models. One emergingchallenge is the phenomenon of Faux Hate, anew type of hate speech that originates fromthe intersection of fake narratives and hatespeech. Faux Hate occurs when fabricatedclaims fuel the generation of hateful language,often blurring the line between misinforma-tion and malicious intent. Identifying suchspeech becomes especially difficult when thefake claim itself is not immediately apparent.This paper provides an overview of a sharedtask competition focused on detecting FauxHate, where participants were tasked with de-veloping methodologies to identify this nu-anced form of harmful speech.</abstract>
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%0 Conference Proceedings
%T Shared Feature-Based Multitask Model for Faux-Hate Classification in Code-Mixed Text
%A Kavatagi, Sanjana
%A Rachh, Rashmi
%A Hiremath, Prakul
%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 kavatagi-etal-2024-shared
%X In recent years, the rise of harmful narratives online has highlighted the need for advancedhate speech detection models. One emergingchallenge is the phenomenon of Faux Hate, anew type of hate speech that originates fromthe intersection of fake narratives and hatespeech. Faux Hate occurs when fabricatedclaims fuel the generation of hateful language,often blurring the line between misinforma-tion and malicious intent. Identifying suchspeech becomes especially difficult when thefake claim itself is not immediately apparent.This paper provides an overview of a sharedtask competition focused on detecting FauxHate, where participants were tasked with de-veloping methodologies to identify this nu-anced form of harmful speech.
%U https://aclanthology.org/2024.icon-fauxhate.12/
%P 61-65

Markdown (Informal)

[Shared Feature-Based Multitask Model for Faux-Hate Classification in Code-Mixed Text](https://aclanthology.org/2024.icon-fauxhate.12/) (Kavatagi et al., ICON 2024)

• Shared Feature-Based Multitask Model for Faux-Hate Classification in Code-Mixed Text (Kavatagi et al., ICON 2024)

ACL

• Sanjana Kavatagi, Rashmi Rachh, and Prakul Hiremath. 2024. Shared Feature-Based Multitask Model for Faux-Hate Classification in Code-Mixed Text. 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 61–65, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).