@inproceedings{rahman-etal-2025-msm,
    title = "{MSM}{\_}{CUET}@{D}ravidian{L}ang{T}ech 2025: {XLM}-{BERT} and {M}u{RIL} Based Transformer Models for Detection of Abusive {T}amil and {M}alayalam Text Targeting Women on Social Media",
    author = "Rahman, Md Mizanur  and
      Dhar, Srijita  and
      Hasan, Md Mehedi  and
      Murad, Hasan",
    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/moar-dois/2025.dravidianlangtech-1.42/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.42",
    pages = "243--247",
    ISBN = "979-8-89176-228-2",
    abstract = "Social media has evolved into an excellent platform for presenting ideas, viewpoints, and experiences in modern society. But this large domain has also brought some alarming problems including internet misuse. Targeted specifically at certain groups like women, abusive language is pervasive on social media. The task is always difficult to detect abusive text for low-resource languages like Tamil, Malayalam, and other Dravidian languages. It is crucial to address this issue seriously, especially for Dravidian languages. This paper presents a novel approach to detecting abusive Tamil and Malayalam texts targeting social media. A shared task on Abusive Tamil and Malayalam Text Targeting Women on Social Media Detection has been organized by DravidianLangTech at NAACL-2025. The organizer has provided an annotated dataset that labels two classes: Abusive and Non-Abusive. We have implemented our model with different transformer-based models like XLM-R, MuRIL, IndicBERT, and mBERT transformers and the Ensemble method with SVM and Random Forest for training. We selected XLM-RoBERT for Tamil text and MuRIL for Malayalam text due to their superior performance compared to other models. After developing our model, we tested and evaluated it on the DravidianLangTech@NAACL 2025 shared task dataset. We found that XLM-R has provided the best result for abusive Tamil text detections with an F1 score of 0.7873 on the test set and ranked 2nd position among all participants. On the other hand, MuRIL has provided the best result for abusive Malayalam text detections with an F1 score of 0.6812 and ranked 10th among all participants."
}