@inproceedings{tofa-etal-2025-cuet-novice,
    title = "{CUET}{\_}{N}ovice@{D}ravidian{L}ang{T}ech 2025: Abusive Comment Detection in {M}alayalam Text Targeting Women on Social Media Using Transformer-Based Models",
    author = "Tofa, Farjana Alam  and
      Sayma, Khadiza Sultana  and
      Osama, Md  and
      Dey, Ashim",
    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-1.85/",
    pages = "483--488",
    ISBN = "979-8-89176-228-2",
    abstract = "Social media has become a widely used platform for communication and entertainment, but it has also become a space where abuseand harassment can thrive. Women, in particular, face hateful and abusive comments that reflect gender inequality. This paper discussesour participation in the Abusive Text Targeting Women in Dravidian Languages shared task at DravidianLangTech@NAACL 2025, whichfocuses on detecting abusive text targeting women in Malayalam social media comments. The shared task provided a dataset of YouTubecomments in Tamil and Malayalam, focusing on sensitive and controversial topics where abusive behavior is prevalent. Our participationfocused on the Malayalam dataset, where the goal was to classify comments into these categories accurately. Malayalam-BERT achievedthe best performance on the subtask, securing 3rd place with a macro f1-score of 0.7083, highlighting the effectiveness of transformer models for low-resource languages. These results contribute to tackling gender-based abuse and improving online content moderation for underrepresented languages."
}