@inproceedings{achamaleh-etal-2025-cic-nlp,
title = "{CIC}-{NLP}@{D}ravidian{L}ang{T}ech 2025: Fake News Detection in {D}ravidian Languages",
author = "Achamaleh, Tewodros and
Hafeez, Nida and
Mebraihtu, Mikiyas and
Uroosa, Fatima and
Sidorov, Grigori",
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-1.111/",
doi = "10.18653/v1/2025.dravidianlangtech-1.111",
pages = "647--654",
ISBN = "979-8-89176-228-2",
abstract = "Misinformation is a growing problem for technologycompanies and for society. Although there exists a large body of related work on identifying fake news in predominantlyresource languages, there is unfortunately a lack of such studies in low-resource languages (LRLs). Because corpora and annotated data are scarce in LRLs, the identification of false information remains at an exploratory stage. Fake news detection is critical in this digital era to avoid spreading misleading information. This research work presents an approach to Detect Fake News in Dravidian Languages. Our team CIC-NLP work primarily targets Task 1 which involves identifying whether a given social platform news is original or fake. For fake news detection (FND) problem, we used mBERT model and utilized the dataset that was provided by the organizers of the workshop. In this work, we describe our findings and the results of the proposed method. Our mBERT model achieved an F1 score of 0.853."
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%0 Conference Proceedings
%T CIC-NLP@DravidianLangTech 2025: Fake News Detection in Dravidian Languages
%A Achamaleh, Tewodros
%A Hafeez, Nida
%A Mebraihtu, Mikiyas
%A Uroosa, Fatima
%A Sidorov, Grigori
%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 achamaleh-etal-2025-cic-nlp
%X Misinformation is a growing problem for technologycompanies and for society. Although there exists a large body of related work on identifying fake news in predominantlyresource languages, there is unfortunately a lack of such studies in low-resource languages (LRLs). Because corpora and annotated data are scarce in LRLs, the identification of false information remains at an exploratory stage. Fake news detection is critical in this digital era to avoid spreading misleading information. This research work presents an approach to Detect Fake News in Dravidian Languages. Our team CIC-NLP work primarily targets Task 1 which involves identifying whether a given social platform news is original or fake. For fake news detection (FND) problem, we used mBERT model and utilized the dataset that was provided by the organizers of the workshop. In this work, we describe our findings and the results of the proposed method. Our mBERT model achieved an F1 score of 0.853.
%R 10.18653/v1/2025.dravidianlangtech-1.111
%U https://aclanthology.org/2025.dravidianlangtech-1.111/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.111
%P 647-654
Markdown (Informal)
[CIC-NLP@DravidianLangTech 2025: Fake News Detection in Dravidian Languages](https://aclanthology.org/2025.dravidianlangtech-1.111/) (Achamaleh et al., DravidianLangTech 2025)
ACL
- Tewodros Achamaleh, Nida Hafeez, Mikiyas Mebraihtu, Fatima Uroosa, and Grigori Sidorov. 2025. CIC-NLP@DravidianLangTech 2025: Fake News Detection in Dravidian Languages. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 647–654, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.