@inproceedings{johnson-etal-2025-rmkmavericks,
    title = "{RMKM}avericks@{D}ravidian{L}ang{T}ech 2025: Tackling Abusive {T}amil and {M}alayalam Text Targeting Women: A Linguistic Approach",
    author = "Johnson, Sandra  and
      E, Boomika  and
      P, Lahari",
    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/ingest-emnlp/2025.dravidianlangtech-1.4/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.4",
    pages = "19--23",
    ISBN = "979-8-89176-228-2",
    abstract = "Social media abuse of women is a widespread problem, especially in regional languages like Tamil and Malayalam, where there are few tools for automated identification. The use of machine learning methods to detect abusive messages in several languages is examined in this work. An external dataset was used to train a Support Vector Machine (SVM) model for Tamil, which produced an F1 score of 0.6196. Using the given dataset, a Multinomial Naive Bayes (MNB) model was trained for Malayalam, obtaining an F1 score of 0.6484. Both models processed and analyzed textual input efficiently by using TF-IDF vectorization for feature extraction. This method shows the ability to solve the linguistic diversity and complexity of abusive language identification by utilizing language-specific datasets and customized algorithms. The results highlight how crucial it is to use focused machine learning techniques to make online spaces safer for women, especially when speaking minority languages."
}