@inproceedings{tripty-etal-2024-cuetsentimentsillies-dravidianlangtech,
title = "{CUETS}entiment{S}illies@{D}ravidian{L}ang{T}ech {EACL}2024: Transformer-based Approach for Detecting and Categorizing Fake News in {M}alayalam Language",
author = "Tripty, Zannatul and
Nafis, Md. and
Chowdhury, Antu and
Hossain, Jawad and
Ahsan, Shawly and
Hoque, Mohammed Moshiul",
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-1.41/",
pages = "245--251",
abstract = "Fake news misleads people and may lead to real-world miscommunication and injury. Removing misinformation encourages critical thinking, democracy, and the prevention of hatred, fear, and misunderstanding. Identifying and removing fake news and developing a detection system is essential for reliable, accurate, and clear information. Therefore, a shared task was organized to detect fake news in Malayalam. This paper presents a system developed for the shared task of detecting and classifying fake news in Malayalam. The approach involves a combination of machine learning models (LR, DT, RF, MNB), deep learning models (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-BERT, XLMR, Malayalam-BERT, m-BERT) for both subtasks. The experimental results demonstrate that transformer-based models, specifically m- BERT and Malayalam-BERT, outperformed others. The m-BERT model achieved superior performance in subtask 1 with macro F1-scores of 0.84, and Malayalam-BERT outperformed the other models in subtask 2 with macro F1- scores of 0.496, securing us the 5th and 2nd positions in subtask 1 and subtask 2, respectively."
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<abstract>Fake news misleads people and may lead to real-world miscommunication and injury. Removing misinformation encourages critical thinking, democracy, and the prevention of hatred, fear, and misunderstanding. Identifying and removing fake news and developing a detection system is essential for reliable, accurate, and clear information. Therefore, a shared task was organized to detect fake news in Malayalam. This paper presents a system developed for the shared task of detecting and classifying fake news in Malayalam. The approach involves a combination of machine learning models (LR, DT, RF, MNB), deep learning models (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-BERT, XLMR, Malayalam-BERT, m-BERT) for both subtasks. The experimental results demonstrate that transformer-based models, specifically m- BERT and Malayalam-BERT, outperformed others. The m-BERT model achieved superior performance in subtask 1 with macro F1-scores of 0.84, and Malayalam-BERT outperformed the other models in subtask 2 with macro F1- scores of 0.496, securing us the 5th and 2nd positions in subtask 1 and subtask 2, respectively.</abstract>
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%0 Conference Proceedings
%T CUETSentimentSillies@DravidianLangTech EACL2024: Transformer-based Approach for Detecting and Categorizing Fake News in Malayalam Language
%A Tripty, Zannatul
%A Nafis, Md.
%A Chowdhury, Antu
%A Hossain, Jawad
%A Ahsan, Shawly
%A Hoque, Mohammed Moshiul
%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 tripty-etal-2024-cuetsentimentsillies-dravidianlangtech
%X Fake news misleads people and may lead to real-world miscommunication and injury. Removing misinformation encourages critical thinking, democracy, and the prevention of hatred, fear, and misunderstanding. Identifying and removing fake news and developing a detection system is essential for reliable, accurate, and clear information. Therefore, a shared task was organized to detect fake news in Malayalam. This paper presents a system developed for the shared task of detecting and classifying fake news in Malayalam. The approach involves a combination of machine learning models (LR, DT, RF, MNB), deep learning models (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-BERT, XLMR, Malayalam-BERT, m-BERT) for both subtasks. The experimental results demonstrate that transformer-based models, specifically m- BERT and Malayalam-BERT, outperformed others. The m-BERT model achieved superior performance in subtask 1 with macro F1-scores of 0.84, and Malayalam-BERT outperformed the other models in subtask 2 with macro F1- scores of 0.496, securing us the 5th and 2nd positions in subtask 1 and subtask 2, respectively.
%U https://aclanthology.org/2024.dravidianlangtech-1.41/
%P 245-251
Markdown (Informal)
[CUETSentimentSillies@DravidianLangTech EACL2024: Transformer-based Approach for Detecting and Categorizing Fake News in Malayalam Language](https://aclanthology.org/2024.dravidianlangtech-1.41/) (Tripty et al., DravidianLangTech 2024)
ACL
- Zannatul Tripty, Md. Nafis, Antu Chowdhury, Jawad Hossain, Shawly Ahsan, and Mohammed Moshiul Hoque. 2024. CUETSentimentSillies@DravidianLangTech EACL2024: Transformer-based Approach for Detecting and Categorizing Fake News in Malayalam Language. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 245–251, St. Julian's, Malta. Association for Computational Linguistics.