@inproceedings{sakib-etal-2025-cuet,
title = "{CUET}-{NLP}{\_}{B}ig{\_}{O}@{D}ravidian{L}ang{T}ech 2025: A {BERT}-based Approach to Detect Fake News from {M}alayalam Social Media Texts",
author = "Sakib, Nazmus and
Hossan, Md. Refaj and
Hossain, Alamgir and
Hossain, Jawad and
Hoque, Mohammed Moshiul",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.78/",
doi = "10.18653/v1/2025.dravidianlangtech-1.78",
pages = "440--447",
ISBN = "979-8-89176-228-2",
abstract = {The rapid growth of digital platforms and social media has significantly contributed to spreading fake news, posing serious societal challenges. While extensive research has been conducted on detecting fake news in high-resource languages (HRLs) such as English, relatively little attention has been given to low-resource languages (LRLs) like Malayalam due to insufficient data and computational tools. To address this challenge, the DravidianLangTech 2025 workshop organized a shared task on fake news detection in Dravidian languages. The task was divided into two sub-tasks, and our team participated in Task 1, which focused on classifying social media texts as original or fake. We explored a range of machine learning (ML) techniques, including Logistic Regression (LR), Multinomial Na{\"i}ve Bayes (MNB), and Support Vector Machines (SVM), as well as deep learning (DL) models such as CNN, BiLSTM, and a hybrid CNN+BiLSTM. Additionally, this work examined several transformer-based models, including m-BERT, Indic-BERT, XLM-Roberta, and MuRIL-BERT, to exploit the task. Our team achieved 6th place in Task 1, with MuRIL-BERT delivering the best performance, achieving an F1 score of 0.874.}
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%0 Conference Proceedings
%T CUET-NLP_Big_O@DravidianLangTech 2025: A BERT-based Approach to Detect Fake News from Malayalam Social Media Texts
%A Sakib, Nazmus
%A Hossan, Md. Refaj
%A Hossain, Alamgir
%A Hossain, Jawad
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F sakib-etal-2025-cuet
%X The rapid growth of digital platforms and social media has significantly contributed to spreading fake news, posing serious societal challenges. While extensive research has been conducted on detecting fake news in high-resource languages (HRLs) such as English, relatively little attention has been given to low-resource languages (LRLs) like Malayalam due to insufficient data and computational tools. To address this challenge, the DravidianLangTech 2025 workshop organized a shared task on fake news detection in Dravidian languages. The task was divided into two sub-tasks, and our team participated in Task 1, which focused on classifying social media texts as original or fake. We explored a range of machine learning (ML) techniques, including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), and Support Vector Machines (SVM), as well as deep learning (DL) models such as CNN, BiLSTM, and a hybrid CNN+BiLSTM. Additionally, this work examined several transformer-based models, including m-BERT, Indic-BERT, XLM-Roberta, and MuRIL-BERT, to exploit the task. Our team achieved 6th place in Task 1, with MuRIL-BERT delivering the best performance, achieving an F1 score of 0.874.
%R 10.18653/v1/2025.dravidianlangtech-1.78
%U https://aclanthology.org/2025.dravidianlangtech-1.78/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.78
%P 440-447
Markdown (Informal)
[CUET-NLP_Big_O@DravidianLangTech 2025: A BERT-based Approach to Detect Fake News from Malayalam Social Media Texts](https://aclanthology.org/2025.dravidianlangtech-1.78/) (Sakib et al., DravidianLangTech 2025)
ACL
- Nazmus Sakib, Md. Refaj Hossan, Alamgir Hossain, Jawad Hossain, and Mohammed Moshiul Hoque. 2025. CUET-NLP_Big_O@DravidianLangTech 2025: A BERT-based Approach to Detect Fake News from Malayalam Social Media Texts. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 440–447, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.