@inproceedings{nayel-etal-2025-nayel,
    title = "{NAYEL}@{D}ravidian{L}ang{T}ech-2025: Character N-gram and Machine Learning Coordination for Fake News Detection in {D}ravidian Languages",
    author = "Nayel, Hamada  and
      Aldawsari, Mohammed  and
      Shashirekha, Hosahalli Lakshmaiah",
    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-1.103/",
    pages = "605--608",
    ISBN = "979-8-89176-228-2",
    abstract = "This paper introduces the detailed description of the submitted model by the team NAYEL to Fake News Detection in Dravidian Languages shared task. The proposed model uses a simple character n-gram TF-IDF as a feature extraction approach integrated with an ensemble of various classical machine learning classification algorithms. While the simplicity of the proposed model structure, although it outperforms other complex structure models as the shared task results observed. The proposed model achieved a f1-score of 87.5{\%} and secured the 5th rank."
}