@inproceedings{g-etal-2025-linguaists,
    title = "{L}ingu{AI}sts@{D}ravidian{L}ang{T}ech 2025: Abusive {T}amil and {M}alayalam Text targeting Women on Social Media",
    author = "G, Dhanyashree  and
      K, Kalpana  and
      A, Lekhashree  and
      K, Arivuchudar  and
      R, Arthi  and
      Sahitya, Bommineni  and
      J, Pavithra  and
      Johnson, Sandra",
    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/fix-sig-urls/2025.dravidianlangtech-1.51/",
    pages = "293--298",
    ISBN = "979-8-89176-228-2",
    abstract = "Social media sites are becoming crucial sites for communication and interaction, yet they are increasingly being utilized to commit gender-based abuse, with horrific, harassing, and degrading comments targeted at women. This paper tries to solve the common issue of women being subjected to abusive language in two South Indian languages, Malayalam and Tamil. To find explicit abuse, implicit bias, preconceptions, and coded language, we were given a set of YouTube comments labeled Abusive and Non-Abusive. To solve this problem, we applied and compared different machine learning models, i.e., Support Vector Machines (SVM), Logistic Regression (LR), and Naive Bayes classifiers, to classify comments into the given categories. The models were trained and validated using the given dataset to achieve the best performance with respect to accuracy and macro F1 score. The solutions proposed aim to make robust content moderation systems that can detect and prevent abusive language, ensuring safer online environments for women."
}