@inproceedings{kabir-etal-2025-cuet,
title = "{CUET}-{NLP}{\_}{MP}@{D}ravidian{L}ang{T}ech 2025: A Transformer and {LLM}-Based Ensemble Approach for Fake News Detection in {D}ravidian",
author = "Kabir, Md Minhazul and
Mohiuddin, Md. and
Ahmed, Kawsar 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-1.75/",
doi = "10.18653/v1/2025.dravidianlangtech-1.75",
pages = "420--426",
ISBN = "979-8-89176-228-2",
abstract = "Fake news detection is a critical problem in today{'}s digital age, aiming to classify intentionally misleading or fabricated news content. In this study, we present a transformer and LLM-based ensemble method to address the challenges in fake news detection. We explored various machine learning (ML), deep learning (DL), transformer, and LLM-based approaches on a Malayalam fake news detection dataset. Our findings highlight the difficulties faced by traditional ML and DL methods in accurately detecting fake news, while transformer- and LLM-based ensemble methods demonstrate significant improvements in performance. The ensemble method combining Sarvam-1, Malayalam-BERT, and XLM-R outperformed all other approaches, achieving an F1-score of 89.30{\%} on the given dataset. This accomplishment, which contributed to securing 2nd place in the shared task at DravidianLangTech 2025, underscores the importance of developing effective methods for detecting fake news in Dravidian languages."
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%0 Conference Proceedings
%T CUET-NLP_MP@DravidianLangTech 2025: A Transformer and LLM-Based Ensemble Approach for Fake News Detection in Dravidian
%A Kabir, Md Minhazul
%A Mohiuddin, Md.
%A Ahmed, Kawsar
%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 kabir-etal-2025-cuet
%X Fake news detection is a critical problem in today’s digital age, aiming to classify intentionally misleading or fabricated news content. In this study, we present a transformer and LLM-based ensemble method to address the challenges in fake news detection. We explored various machine learning (ML), deep learning (DL), transformer, and LLM-based approaches on a Malayalam fake news detection dataset. Our findings highlight the difficulties faced by traditional ML and DL methods in accurately detecting fake news, while transformer- and LLM-based ensemble methods demonstrate significant improvements in performance. The ensemble method combining Sarvam-1, Malayalam-BERT, and XLM-R outperformed all other approaches, achieving an F1-score of 89.30% on the given dataset. This accomplishment, which contributed to securing 2nd place in the shared task at DravidianLangTech 2025, underscores the importance of developing effective methods for detecting fake news in Dravidian languages.
%R 10.18653/v1/2025.dravidianlangtech-1.75
%U https://aclanthology.org/2025.dravidianlangtech-1.75/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.75
%P 420-426
Markdown (Informal)
[CUET-NLP_MP@DravidianLangTech 2025: A Transformer and LLM-Based Ensemble Approach for Fake News Detection in Dravidian](https://aclanthology.org/2025.dravidianlangtech-1.75/) (Kabir et al., DravidianLangTech 2025)
ACL
- Md Minhazul Kabir, Md. Mohiuddin, Kawsar Ahmed, and Mohammed Moshiul Hoque. 2025. CUET-NLP_MP@DravidianLangTech 2025: A Transformer and LLM-Based Ensemble Approach for Fake News Detection in Dravidian. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 420–426, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.