CUET_Binary_Hackers@DravidianLangTech EACL2024: Fake News Detection in Malayalam Language Leveraging Fine-tuned MuRIL BERT

Salman Farsi, Asrarul Eusha, Ariful Islam, Hasan Mesbaul Ali Taher, Jawad Hossain, Shawly Ahsan, Avishek Das, Mohammed Moshiul Hoque


Abstract
Due to technological advancements, various methods have emerged for disseminating news to the masses. The pervasive reach of news, however, has given rise to a significant concern: the proliferation of fake news. In response to this challenge, a shared task in Dravidian- LangTech EACL2024 was initiated to detect fake news and classify its types in the Malayalam language. The shared task consisted of two sub-tasks. Task 1 focused on a binary classification problem, determining whether a piece of news is fake or not. Whereas task 2 delved into a multi-class classification problem, categorizing news into five distinct levels. Our approach involved the exploration of various machine learning (RF, SVM, XGBoost, Ensemble), deep learning (BiLSTM, CNN), and transformer-based models (MuRIL, Indic- SBERT, m-BERT, XLM-R, Distil-BERT) by emphasizing parameter tuning to enhance overall model performance. As a result, we introduce a fine-tuned MuRIL model that leverages parameter tuning, achieving notable success with an F1-score of 0.86 in task 1 and 0.5191 in task 2. This successful implementation led to our system securing the 3rd position in task 1 and the 1st position in task 2. The source code will be found in the GitHub repository at this link: https://github.com/Salman1804102/ DravidianLangTech-EACL-2024-FakeNews.
Anthology ID:
2024.dravidianlangtech-1.29
Volume:
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
Month:
March
Year:
2024
Address:
St. Julian's, Malta
Editors:
Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar Madasamy, Sajeetha Thavareesan, Elizabeth Sherly, Rajeswari Nadarajan, Manikandan Ravikiran
Venues:
DravidianLangTech | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
173–179
Language:
URL:
https://aclanthology.org/2024.dravidianlangtech-1.29
DOI:
Bibkey:
Cite (ACL):
Salman Farsi, Asrarul Eusha, Ariful Islam, Hasan Mesbaul Ali Taher, Jawad Hossain, Shawly Ahsan, Avishek Das, and Mohammed Moshiul Hoque. 2024. CUET_Binary_Hackers@DravidianLangTech EACL2024: Fake News Detection in Malayalam Language Leveraging Fine-tuned MuRIL BERT. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 173–179, St. Julian's, Malta. Association for Computational Linguistics.
Cite (Informal):
CUET_Binary_Hackers@DravidianLangTech EACL2024: Fake News Detection in Malayalam Language Leveraging Fine-tuned MuRIL BERT (Farsi et al., DravidianLangTech-WS 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2024.dravidianlangtech-1.29.pdf