@inproceedings{sankar-rajalakshmi-2026-dlrg,
    title = "{DLRG}@{D}ravidian{L}ang{T}ech 2026: Explainable Transformer-Based Detection of Abusive {T}amil Text Targeting Women on Social Media",
    author = "Sankar, Mirudhula  and
      Rajalakshmi, Ratnavel",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.34/",
    pages = "237--241",
    ISBN = "979-8-89176-401-9",
    abstract = "Many social media platforms have users who have normalized the abuse of women online, creating a need for systems that automatically detect such activity. For low-resource, regional languages like Tamil, which has informal writing styles, spelling variations, dialectal differences, and culturally specific expressions, it becomes a challenge to correctly detect abusive comments. In this work, a transformer-based approach for binary classification of Tamil comments into abusive and non-abusive categories is done using the DravidianLangTech dataset. The proposed system fine-tunes MuRIL(a multilingual transformer pretrained for Indian languages), enabling effective contextual representation with minimal preprocessing. To improve the transparency of the system, a post-hoc Explainable AI component is incorporated. A perturbation-based method using log-odds differences identifies words that significantly influence the predictions. Experimental findings indicate that the model reaches a validation accuracy exceeding 81{\%} while also exhibiting a strong macro-F1 score. This research shows that utilizing contextual multilingual representations alongside simple interpretability methods offers a viable and effective approach for detecting abusive text in Tamil. The implementation of our system is publicly available at https://github.com/mirud5173/Abusive-Tamil-Comment-Detection-using-Transformer-Models"
}