@inproceedings{vetagiri-pakray-2024-detecting,
title = "Detecting Hate Speech and Fake Narratives in Code-Mixed {H}inglish Social Media Text",
author = "Vetagiri, Advaitha and
Pakray, Partha",
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.5/",
pages = "22--28",
abstract = "The increasing prevalence of hate speech and fake narratives on social media platforms posessignificant societal challenges. This study ad-dresses these issues through the developmentof robust machine learning models for twotasks: (1) detecting hate speech and fake nar-ratives (Task A) and (2) predicting the targetand severity of hateful content (Task B) incode-mixed Hindi-English text. We proposefour separate CNN-BiLSTM models tailoredfor each subtask. The models were evaluatedusing validation and 5-fold cross-validationdatasets, achieving F1-scores of 74{\%} and 79{\%}for hate and fake detection, respectively, and63{\%} and 54{\%} for target and severity predic-tion and achieved 65{\%} and 57{\%} for testingresults. The results highlight the models' effec-tiveness in handling the nuances of code-mixedtext while underscoring the challenges of under-represented classes. This work contributes tothe ongoing effort to develop automated toolsfor detecting and mitigating harmful contentonline, paving the way for safer and more in-clusive digital spaces."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="vetagiri-pakray-2024-detecting">
<titleInfo>
<title>Detecting Hate Speech and Fake Narratives in Code-Mixed Hinglish Social Media Text</title>
</titleInfo>
<name type="personal">
<namePart type="given">Advaitha</namePart>
<namePart type="family">Vetagiri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Partha</namePart>
<namePart type="family">Pakray</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 increasing prevalence of hate speech and fake narratives on social media platforms posessignificant societal challenges. This study ad-dresses these issues through the developmentof robust machine learning models for twotasks: (1) detecting hate speech and fake nar-ratives (Task A) and (2) predicting the targetand severity of hateful content (Task B) incode-mixed Hindi-English text. We proposefour separate CNN-BiLSTM models tailoredfor each subtask. The models were evaluatedusing validation and 5-fold cross-validationdatasets, achieving F1-scores of 74% and 79%for hate and fake detection, respectively, and63% and 54% for target and severity predic-tion and achieved 65% and 57% for testingresults. The results highlight the models’ effec-tiveness in handling the nuances of code-mixedtext while underscoring the challenges of under-represented classes. This work contributes tothe ongoing effort to develop automated toolsfor detecting and mitigating harmful contentonline, paving the way for safer and more in-clusive digital spaces.</abstract>
<identifier type="citekey">vetagiri-pakray-2024-detecting</identifier>
<location>
<url>https://aclanthology.org/2024.icon-fauxhate.5/</url>
</location>
<part>
<date>2024-12</date>
<extent unit="page">
<start>22</start>
<end>28</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Detecting Hate Speech and Fake Narratives in Code-Mixed Hinglish Social Media Text
%A Vetagiri, Advaitha
%A Pakray, Partha
%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 vetagiri-pakray-2024-detecting
%X The increasing prevalence of hate speech and fake narratives on social media platforms posessignificant societal challenges. This study ad-dresses these issues through the developmentof robust machine learning models for twotasks: (1) detecting hate speech and fake nar-ratives (Task A) and (2) predicting the targetand severity of hateful content (Task B) incode-mixed Hindi-English text. We proposefour separate CNN-BiLSTM models tailoredfor each subtask. The models were evaluatedusing validation and 5-fold cross-validationdatasets, achieving F1-scores of 74% and 79%for hate and fake detection, respectively, and63% and 54% for target and severity predic-tion and achieved 65% and 57% for testingresults. The results highlight the models’ effec-tiveness in handling the nuances of code-mixedtext while underscoring the challenges of under-represented classes. This work contributes tothe ongoing effort to develop automated toolsfor detecting and mitigating harmful contentonline, paving the way for safer and more in-clusive digital spaces.
%U https://aclanthology.org/2024.icon-fauxhate.5/
%P 22-28
Markdown (Informal)
[Detecting Hate Speech and Fake Narratives in Code-Mixed Hinglish Social Media Text](https://aclanthology.org/2024.icon-fauxhate.5/) (Vetagiri & Pakray, ICON 2024)
ACL