@inproceedings{k-b-2026-tamilgoodbadtxt,
    title = "{TAMILGOODBADTXT}@{D}ravidian{L}ang{T}ech 2026:A Multilingual Transformer-Based Approach for Abusive Language Identification in {T}amil Social Media",
    author = "K, Varalakshmi  and
      B, Bharathi",
    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-1.62/",
    pages = "392--396",
    ISBN = "979-8-89176-401-9",
    abstract = "It is difficult to detect abusive language, particularly in social networks for low-resource languages like Tamil. Spelling errors, informal expressions and code-mixing make it even more challenging to read text from social media. The current work proposes a multilingual transformer-based approach to detect abusive content in Tamil text. A pretrained XLM-RoBERTa model is used to learn contextual and semantic representations from the input text. This is a general pipeline comprising preprocessing, tokenization, and binary classification (abusive / non-abusive). Experiments are performed on Tamil social media datasets that have abusive and non-abusive data. The results reveal that multilingual transformer models achieve good performance in low-resource scenarios. The proposed model attains an F1-score of 78.64{\%}, which shows the potential of using cross-lingual pretrained models for the detection of abusive Tamil language."
}