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    <abstract>In the digital age, identifying fake news is essential when fake information travels quickly via social media platforms. This project employs machine learning techniques, including Random Forest, Logistic Regression, and Decision Tree, to distinguish between real and fake news. With the rise of news consumption on social media, it becomes essential to authenticate information shared on platforms like YouTube comments. The research emphasizes the need to stop spreading harmful rumors and focuses on authenticating news articles. The proposed model utilizes machine learning and natural language processing, specifically Support Vector Machines, to aggregate and determine the authenticity of news. To address the challenges of detecting fake news in this paper, describe the Machine Learning (ML) models submitted to ‘Fake News Detection in Dravidian Languages” at DravidianLangTech@EACL 2024 shared task. Four different models, namely: Naive Bayes, Support Vector Machine (SVM), Random forest, and Decision tree.</abstract>
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%0 Conference Proceedings
%T Beyond Tech@DravidianLangTech2024 : Fake News Detection in Dravidian Languages Using Machine Learning
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A R, Sanjai
%A Sameer B, Mohammed
%A K, Motheeswaran
%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 shanmugavadivel-etal-2024-beyond
%X In the digital age, identifying fake news is essential when fake information travels quickly via social media platforms. This project employs machine learning techniques, including Random Forest, Logistic Regression, and Decision Tree, to distinguish between real and fake news. With the rise of news consumption on social media, it becomes essential to authenticate information shared on platforms like YouTube comments. The research emphasizes the need to stop spreading harmful rumors and focuses on authenticating news articles. The proposed model utilizes machine learning and natural language processing, specifically Support Vector Machines, to aggregate and determine the authenticity of news. To address the challenges of detecting fake news in this paper, describe the Machine Learning (ML) models submitted to ‘Fake News Detection in Dravidian Languages” at DravidianLangTech@EACL 2024 shared task. Four different models, namely: Naive Bayes, Support Vector Machine (SVM), Random forest, and Decision tree.
%U https://aclanthology.org/2024.dravidianlangtech-1.20
%P 124-128

Markdown (Informal)

[Beyond Tech@DravidianLangTech2024 : Fake News Detection in Dravidian Languages Using Machine Learning](https://aclanthology.org/2024.dravidianlangtech-1.20) (Shanmugavadivel et al., DravidianLangTech-WS 2024)

• Beyond Tech@DravidianLangTech2024 : Fake News Detection in Dravidian Languages Using Machine Learning (Shanmugavadivel et al., DravidianLangTech-WS 2024)

ACL

• Kogilavani Shanmugavadivel, Malliga Subramanian, Sanjai R, Mohammed Sameer B, and Motheeswaran K. 2024. Beyond Tech@DravidianLangTech2024 : Fake News Detection in Dravidian Languages Using Machine Learning. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 124–128, St. Julian's, Malta. Association for Computational Linguistics.