@inproceedings{nivetha-etal-2026-kecs,
    title = "{KEC}'{S} {CODE} {CRAFTERS}@{D}ravidian{L}ang{T}ech 2026: Abusive {T}amil Text Detection Targeting Women on Social Media",
    author = "Nivetha  and
      S, Nethrasri  and
      Subramanian, Malliga",
    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-1.43/",
    pages = "284--288",
    ISBN = "979-8-89176-401-9",
    abstract = "As social media platforms continue to grow insize, unfortunately, they have also become ahub for digital toxicity, where women in linguistically diverse regions are particularly vulnerable to online harassment. Hence, the requirement for an automated moderation toolthat can effectively handle regional languagesis critical. Our paper is a step in this direction as we propose a classification modelfor the ``Abusive Tamil Text Detection Targeting Women on Social Media'' shared taskfor DravidianLangTech-2026. Our model istrained on a dataset of 25,948 comments fortraining and 915 for testing. Our primary objective was to classify content as either ``Abusive{''}or ``Non-Abusive'' for YouTube videos. TheTamil language is particularly difficult to workwith owing to its highly agglutinative structure and the tendency for code-mixing betweenTamil and English or even using a mix of bothin a single sentence. To overcome these difficulties in preprocessing, we designed a specificpipeline for denoising these informal scripts.We then implemented four traditional machinelearning models: SVM, Logistic Regression,Random Forest, and Multinomial Naive Bayesusing TF-IDF for feature extraction. Our modelwas optimized for hyperparameters and decision thresholds to achieve an accuracy and F1score of 0.86 using Logistic Regression"
}