@inproceedings{c-v-etal-2024-faux,
title = "Faux-Hate Multitask Framework for Misinformation and Hate Speech Detection in Code-Mixed Languages",
author = "C V, Sunil Gopal and
S, Sudhan and
Srinivas, Shreyas Gutti and
R, Sushanth and
C B, Abhilash",
editor = "Biradar, Shankar and
Reddy, Kasu Sai Kartheek and
Saumya, Sunil and
Akhtar, Md. Shad",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-fauxhate.9/",
pages = "45--49",
abstract = "The Faux-Hate task looks at two big issues:misinformation and hate speech. It focuses onHindi-English social media posts. This papershares our methods for both parts of the task.For Task A, we built a special model based onXLM-RoBERTa. It has features that help usspot both fake news and hate speech at the sametime. For Task B, we wanted to identify whothe hate is aimed at (like individuals or groups)and how severe it is (high, medium, low). So,we added different tools to our model for thiskind of sorting. To get ready for all this, wecarefully cleaned the data, especially dealingwith mixing languages and different spellings.In Task A, our results show that our model canclearly tell the difference between fake and realstories, as well as between hate and non-hatespeech. For Task B, it does a good job withidentifying targets and severity levels, givingstrong predictions for multiple labels. Overall,these results show that cross-lingual models,combined with specific tweaks, can really helptackle complex text classification in languageswith fewer resources."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="c-v-etal-2024-faux">
<titleInfo>
<title>Faux-Hate Multitask Framework for Misinformation and Hate Speech Detection in Code-Mixed Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sunil</namePart>
<namePart type="given">Gopal</namePart>
<namePart type="family">C V</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sudhan</namePart>
<namePart type="family">S</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shreyas</namePart>
<namePart type="given">Gutti</namePart>
<namePart type="family">Srinivas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sushanth</namePart>
<namePart type="family">R</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abhilash</namePart>
<namePart type="family">C B</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<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>
</titleInfo>
<name type="personal">
<namePart type="given">Shankar</namePart>
<namePart type="family">Biradar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kasu</namePart>
<namePart type="given">Sai</namePart>
<namePart type="given">Kartheek</namePart>
<namePart type="family">Reddy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunil</namePart>
<namePart type="family">Saumya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Md.</namePart>
<namePart type="given">Shad</namePart>
<namePart type="family">Akhtar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>NLP Association of India (NLPAI)</publisher>
<place>
<placeTerm type="text">AU-KBC Research Centre, Chennai, India</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The Faux-Hate task looks at two big issues:misinformation and hate speech. It focuses onHindi-English social media posts. This papershares our methods for both parts of the task.For Task A, we built a special model based onXLM-RoBERTa. It has features that help usspot both fake news and hate speech at the sametime. For Task B, we wanted to identify whothe hate is aimed at (like individuals or groups)and how severe it is (high, medium, low). So,we added different tools to our model for thiskind of sorting. To get ready for all this, wecarefully cleaned the data, especially dealingwith mixing languages and different spellings.In Task A, our results show that our model canclearly tell the difference between fake and realstories, as well as between hate and non-hatespeech. For Task B, it does a good job withidentifying targets and severity levels, givingstrong predictions for multiple labels. Overall,these results show that cross-lingual models,combined with specific tweaks, can really helptackle complex text classification in languageswith fewer resources.</abstract>
<identifier type="citekey">c-v-etal-2024-faux</identifier>
<location>
<url>https://aclanthology.org/2024.icon-fauxhate.9/</url>
</location>
<part>
<date>2024-12</date>
<extent unit="page">
<start>45</start>
<end>49</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Faux-Hate Multitask Framework for Misinformation and Hate Speech Detection in Code-Mixed Languages
%A C V, Sunil Gopal
%A S, Sudhan
%A Srinivas, Shreyas Gutti
%A R, Sushanth
%A C B, Abhilash
%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 c-v-etal-2024-faux
%X The Faux-Hate task looks at two big issues:misinformation and hate speech. It focuses onHindi-English social media posts. This papershares our methods for both parts of the task.For Task A, we built a special model based onXLM-RoBERTa. It has features that help usspot both fake news and hate speech at the sametime. For Task B, we wanted to identify whothe hate is aimed at (like individuals or groups)and how severe it is (high, medium, low). So,we added different tools to our model for thiskind of sorting. To get ready for all this, wecarefully cleaned the data, especially dealingwith mixing languages and different spellings.In Task A, our results show that our model canclearly tell the difference between fake and realstories, as well as between hate and non-hatespeech. For Task B, it does a good job withidentifying targets and severity levels, givingstrong predictions for multiple labels. Overall,these results show that cross-lingual models,combined with specific tweaks, can really helptackle complex text classification in languageswith fewer resources.
%U https://aclanthology.org/2024.icon-fauxhate.9/
%P 45-49
Markdown (Informal)
[Faux-Hate Multitask Framework for Misinformation and Hate Speech Detection in Code-Mixed Languages](https://aclanthology.org/2024.icon-fauxhate.9/) (C V et al., ICON 2024)
ACL