@inproceedings{anbalagan-etal-2024-wordwizards,
title = "{W}ord{W}izards@{D}ravidian{L}ang{T}ech 2024:Fake News Detection in {D}ravidian Languages using Cross-lingual Sentence Embeddings",
author = "Anbalagan, Akshatha and
T, Priyadharshini and
A, Niranjana and
Balaji, Shreedevi and
Thenmozhi, Durairaj",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.27/",
pages = "162--166",
abstract = "The proliferation of fake news in digital media has become a significant societal concern, impacting public opinion, trust, and decision-making. This project focuses on the development of machine learning models for the detection of fake news. Leveraging a dataset containing both genuine and deceptive news articles, the proposed models employ natural language processing techniques, feature extraction and classification algorithms. This paper provides a solution to Fake News Detection in Dravidian Languages - DravidianLangTech 2024. There are two sub tasks: Task 1 - The goal of this task is to classify a given social media text into original or fake. We propose an approach for this with the help of a supervised machine learning model {--} SVM (Support Vector Machine). The SVM classifier achieved a macro F1 score of 0.78 in test data and a rank 11. The Task 2 is classifying fake news articles in Malayalam language into different categories namely False, Half True, Mostly False, Partly False and Mostly True.We have used Naive Bayes which achieved macro F1-score 0.3517 in test data and a rank 6."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="anbalagan-etal-2024-wordwizards">
<titleInfo>
<title>WordWizards@DravidianLangTech 2024:Fake News Detection in Dravidian Languages using Cross-lingual Sentence Embeddings</title>
</titleInfo>
<name type="personal">
<namePart type="given">Akshatha</namePart>
<namePart type="family">Anbalagan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Priyadharshini</namePart>
<namePart type="family">T</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Niranjana</namePart>
<namePart type="family">A</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shreedevi</namePart>
<namePart type="family">Balaji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Durairaj</namePart>
<namePart type="family">Thenmozhi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Bharathi</namePart>
<namePart type="given">Raja</namePart>
<namePart type="family">Chakravarthi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruba</namePart>
<namePart type="family">Priyadharshini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anand</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Madasamy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sajeetha</namePart>
<namePart type="family">Thavareesan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Sherly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rajeswari</namePart>
<namePart type="family">Nadarajan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manikandan</namePart>
<namePart type="family">Ravikiran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">St. Julian’s, Malta</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The proliferation of fake news in digital media has become a significant societal concern, impacting public opinion, trust, and decision-making. This project focuses on the development of machine learning models for the detection of fake news. Leveraging a dataset containing both genuine and deceptive news articles, the proposed models employ natural language processing techniques, feature extraction and classification algorithms. This paper provides a solution to Fake News Detection in Dravidian Languages - DravidianLangTech 2024. There are two sub tasks: Task 1 - The goal of this task is to classify a given social media text into original or fake. We propose an approach for this with the help of a supervised machine learning model – SVM (Support Vector Machine). The SVM classifier achieved a macro F1 score of 0.78 in test data and a rank 11. The Task 2 is classifying fake news articles in Malayalam language into different categories namely False, Half True, Mostly False, Partly False and Mostly True.We have used Naive Bayes which achieved macro F1-score 0.3517 in test data and a rank 6.</abstract>
<identifier type="citekey">anbalagan-etal-2024-wordwizards</identifier>
<location>
<url>https://aclanthology.org/2024.dravidianlangtech-1.27/</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>162</start>
<end>166</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T WordWizards@DravidianLangTech 2024:Fake News Detection in Dravidian Languages using Cross-lingual Sentence Embeddings
%A Anbalagan, Akshatha
%A T, Priyadharshini
%A A, Niranjana
%A Balaji, Shreedevi
%A Thenmozhi, Durairaj
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F anbalagan-etal-2024-wordwizards
%X The proliferation of fake news in digital media has become a significant societal concern, impacting public opinion, trust, and decision-making. This project focuses on the development of machine learning models for the detection of fake news. Leveraging a dataset containing both genuine and deceptive news articles, the proposed models employ natural language processing techniques, feature extraction and classification algorithms. This paper provides a solution to Fake News Detection in Dravidian Languages - DravidianLangTech 2024. There are two sub tasks: Task 1 - The goal of this task is to classify a given social media text into original or fake. We propose an approach for this with the help of a supervised machine learning model – SVM (Support Vector Machine). The SVM classifier achieved a macro F1 score of 0.78 in test data and a rank 11. The Task 2 is classifying fake news articles in Malayalam language into different categories namely False, Half True, Mostly False, Partly False and Mostly True.We have used Naive Bayes which achieved macro F1-score 0.3517 in test data and a rank 6.
%U https://aclanthology.org/2024.dravidianlangtech-1.27/
%P 162-166
Markdown (Informal)
[WordWizards@DravidianLangTech 2024:Fake News Detection in Dravidian Languages using Cross-lingual Sentence Embeddings](https://aclanthology.org/2024.dravidianlangtech-1.27/) (Anbalagan et al., DravidianLangTech 2024)
ACL