@inproceedings{vaidyanathan-etal-2025-nlp,
    title = "{NLP}{\_}goats@{D}ravidian{L}ang{T}ech 2025: Towards Safer Social Media: Detecting Abusive Language Directed at Women in {D}ravidian Languages",
    author = "Vaidyanathan, Vijay Karthick  and
      K, Srihari V  and
      Durairaj, Thenmozhi",
    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/landing_page/2025.dravidianlangtech-1.62/",
    pages = "350--354",
    ISBN = "979-8-89176-228-2",
    abstract = "Social media in the present world is an essential communication platform for information sharing. But their emergence has now led to an increase in the proportion of online abuse, in particular against women in the form of abusive and offensive messages. A reflection of the social inequalities, the importance of detecting abusive language is highlighted by the fact that the usage has a profound psychological and social impact on the victims. This work by DravidianLangTech@NAACL 2025 aims at developing an automated abusive content detection system for women directed towards women on the Tamil and Malayalam platforms, two of the Dravidian languages. Based on a dataset of their YouTube comments about sensitive issues, the study uses multilingual BERT (mBERT) to detect abusive comments versus non-abusive ones. We achieved F1 scores of 0.75 in Tamil and 0.68 in Malayalam, placing us 13 and 9 respectively."
}