@inproceedings{shanmugavadivel-etal-2025-blueray,
    title = "{B}lue{R}ay@{D}ravidian{L}ang{T}ech-2025: Fake News Detection in {D}ravidian Languages",
    author = "Shanmugavadivel, Kogilavani  and
      Subramanian, Malliga  and
      M, Aiswarya  and
      T, Aruna  and
      S, Jeevaananth",
    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-1.39/",
    pages = "226--231",
    ISBN = "979-8-89176-228-2",
    abstract = "The rise of fake news presents significant issues, particularly for underrepresented lan guages. This study tackles fake news identification in Dravidian languages with two subtasks: binary classification of YouTube comments and multi-class classification of Malayalam news into five groups. Text preprocessing, vectorization, and transformer-based embeddings are all part of the methodology, including baseline comparisons utilizing classic machine learning, deep learning, and transfer learning models. In Task 1, our solution placed 17th, displaying acceptable binary classification per formance. In Task 2, we finished eighth place by effectively identifying nuanced categories of Malayalam news, demonstrating the efficacy of transformer-based models."
}