@inproceedings{shanmugavadivel-etal-2025-kec-ai-vss,
    title = "{KEC}{\_}{AI}{\_}{VSS}{\_}run2@{D}ravidian{L}ang{T}ech 2025: Abusive {T}amil and {M}alayalam Text targeting Women on Social Media",
    author = "Shanmugavadivel, Kogilavani  and
      Subramanian, Malliga  and
      S, Sathiyaseelan  and
      K, Suresh Babu  and
      S, Vasikaran",
    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-1.34/",
    pages = "200--204",
    ISBN = "979-8-89176-228-2",
    abstract = "The increasing instances of abusive language against women on social media platforms have brought to the fore the need for effective content moderation systems, especially in low-resource languages like Tamil and Malayalam. This paper addresses the challenge of detecting gender-based abuse in YouTube comments using annotated datasets in these languages. Comments are classified into abusive and non-abusive categories. We applied the following machine learning algorithms, namely Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting and AdaBoost for classification. Micro F1 score of 0.95 was achieved by SVM for Tamil and 0.72 by Random Forest for Malayalam. Our system participated in the shared task on abusive comment detection, out of 160 teams achieving the rank of 13th for Malayalam and rank 34 for Tamil, and both indicate both the challenges and potential of our approach in low-resource language processing. Our findings have highlighted the significance of tailored approaches to language-specific abuse detection."
}