@inproceedings{vp-etal-2025-cyber,
    title = "Cyber Protectors@{D}ravidian{L}ang{T}ech 2025: Abusive {T}amil and {M}alayalam Text Targeting Women on Social Media using {F}ast{T}ext",
    author = "Vp, Rohit  and
      M, Madhav  and
      Srichandra, Ippatapu Venkata  and
      Mohan, Neethu  and
      S, Sachin Kumar",
    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-2025-COMPUTEL/2025.dravidianlangtech-1.53/",
    pages = "304--308",
    ISBN = "979-8-89176-228-2",
    abstract = "Social media has transformed communication, but it has opened new ways for women to be abused. Because of complex morphology, large vocabulary, and frequent code-mixing of Tamil and Malayalam, it might be especially challenging to identify discriminatory text in linguistically diverse settings. Because traditional moderation systems frequently miss these linguistic subtleties, gendered abuse in many forms{---}from outright threats to character insults and body shaming{---}continues. In addition to examining the sociocultural characteristics of this type of harassment on social media, this study compares the effectiveness of several Natural Language Processing (NLP) models, such as FastText, transformer-based architectures, and BiLSTM. Our results show that FastText achieved an macro f1 score of 0.74 on the Tamil dataset and 0.64 on the Malayalam dataset, outperforming the Transformer model which achieved a macro f1 score of 0.62 and BiLSTM achieved 0.57. By addressing the limitations of existing moderation techniques, this research underscores the urgent need for language-specific AI solutions to foster safer digital spaces for women."
}