@inproceedings{shanmugavadivel-etal-2025-kec,
    title = "{KEC}{\_}{AI}{\_}{DATA}{\_}{DRIFTERS}@{D}ravidian{L}ang{T}ech 2025: Fake News Detection in {D}ravidian Languages",
    author = "Shanmugavadivel, Kogilavani  and
      Subramanian, Malliga  and
      S, Vishali K  and
      B, Priyanka  and
      K, Naveen Kumar",
    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/Ingest-2025-COMPUTEL/2025.dravidianlangtech-1.29/",
    pages = "173--177",
    ISBN = "979-8-89176-228-2",
    abstract = "Detecting fake news in Malayalam possess significant challenges due to linguistic diversity, code-mixing, and the limited availability of structured datasets. We participated in the Fake News Detection in Dravidian Languages shared task, classifying news and social media posts into binary and multi-class categories. Our experiments used traditional ML models: Support Vector Machine (SVM), Random Forest, Logistic Regression, Naive Bayes and transfer learning models: Multilingual Bert (mBERT) and XLNet. In binary classification, SVM achieved the highest macro-F1 score of 0.97, while in multi-class classification, it also outperformed other models with a macro-F1 score of 0.98. Random Forest ranked second in both tasks. Despite their advanced capabilities, mBERT and XLNet exhibited lower precision due to data limitations. Our approach enhances fake news detection and NLP solutions for low-resource languages."
}