@inproceedings{subramanian-etal-2025-kec,
    title = "{KEC}{\_}{TECH}{\_}{TITANS}@{D}ravidian{L}ang{T}ech 2025: Abusive Text Detection in {T}amil and {M}alayalam Social Media Comments Using Machine Learning",
    author = "Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      P, Deepiga  and
      S, Dharshini  and
      S, Ananthakumar  and
      C, Praveenkumar",
    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-1.45/",
    pages = "259--263",
    ISBN = "979-8-89176-228-2",
    abstract = "Social media platforms have become a breeding ground for hostility and toxicity, with abusive language targeting women becoming a pervasive issue. This paper addresses the detection of abusive content in Tamil and Malayalam social media comments using machine learning models. We experimented with GRU, LSTM, Bidirectional LSTM, CNN, FastText, and XGBoost models, evaluating their performance on a code-mixed dataset of Tamil and Malayalam comments collected from YouTube. Our findings demonstrate that FastText and CNN models yielded the best performance among the evaluated classifiers, achieving F1-scores of 0.73 each. This study contributes to the ongoing research on abusive text detection for under-resourced languages and highlights the need for robust, scalable solutions to combat online toxicity."
}