@inproceedings{subramanian-etal-2025-overview,
title = "Overview of the Shared Task on Fake News Detection in {D}ravidian Languages-{D}ravidian{L}ang{T}ech@{NAACL} 2025",
author = "Subramanian, Malliga and
B, Premjith and
Shanmugavadivel, Kogilavani and
Pandiyan, Santhiya and
Palani, Balasubramanian and
Chakravarthi, Bharathi Raja",
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-1.128/",
doi = "10.18653/v1/2025.dravidianlangtech-1.128",
pages = "759--767",
ISBN = "979-8-89176-228-2",
abstract = {Detecting and mitigating fake news on social media is critical for preventing misinformation, protecting democratic processes, preventing public distress, mitigating hate speech, reducing financial fraud, maintaining information reliability, etc. This paper summarizes the findings of the shared task ``Fake News Detection in Dravidian Languages{---}DravidianLangTech@NAACL 2025.'' The goal of this task is to detect fake content in social media posts in Malayalam. It consists of two subtasks: the first focuses on binary classification (Fake or Original), while the second categorizes the fake news into five types{---}False, Half True, Mostly False, Partly False, and Mostly True. In Task 1, 22 teams submitted machine learning techniques like SVM, Na{\"i}ve Bayes, and SGD, as well as BERT-based architectures. Among these, XLM-RoBERTa had the highest macro F1 score of 89.8{\%}. For Task 2, 11 teams submitted models using LSTM, GRU, XLM-RoBERTa, and SVM. XLM-RoBERTa once again outperformed other models, attaining the highest macro F1 score of 68.2{\%}.}
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%0 Conference Proceedings
%T Overview of the Shared Task on Fake News Detection in Dravidian Languages-DravidianLangTech@NAACL 2025
%A Subramanian, Malliga
%A B, Premjith
%A Shanmugavadivel, Kogilavani
%A Pandiyan, Santhiya
%A Palani, Balasubramanian
%A Chakravarthi, Bharathi Raja
%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 subramanian-etal-2025-overview
%X Detecting and mitigating fake news on social media is critical for preventing misinformation, protecting democratic processes, preventing public distress, mitigating hate speech, reducing financial fraud, maintaining information reliability, etc. This paper summarizes the findings of the shared task “Fake News Detection in Dravidian Languages—DravidianLangTech@NAACL 2025.” The goal of this task is to detect fake content in social media posts in Malayalam. It consists of two subtasks: the first focuses on binary classification (Fake or Original), while the second categorizes the fake news into five types—False, Half True, Mostly False, Partly False, and Mostly True. In Task 1, 22 teams submitted machine learning techniques like SVM, Naïve Bayes, and SGD, as well as BERT-based architectures. Among these, XLM-RoBERTa had the highest macro F1 score of 89.8%. For Task 2, 11 teams submitted models using LSTM, GRU, XLM-RoBERTa, and SVM. XLM-RoBERTa once again outperformed other models, attaining the highest macro F1 score of 68.2%.
%R 10.18653/v1/2025.dravidianlangtech-1.128
%U https://aclanthology.org/2025.dravidianlangtech-1.128/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.128
%P 759-767
Markdown (Informal)
[Overview of the Shared Task on Fake News Detection in Dravidian Languages-DravidianLangTech@NAACL 2025](https://aclanthology.org/2025.dravidianlangtech-1.128/) (Subramanian et al., DravidianLangTech 2025)
ACL
- Malliga Subramanian, Premjith B, Kogilavani Shanmugavadivel, Santhiya Pandiyan, Balasubramanian Palani, and Bharathi Raja Chakravarthi. 2025. Overview of the Shared Task on Fake News Detection in Dravidian Languages-DravidianLangTech@NAACL 2025. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 759–767, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.