@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://preview.aclanthology.org/fix-sig-urls/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."
}