@inproceedings{tash-etal-2024-lidoma,
    title = "Lidoma@{LT}-{EDI} 2024:{T}amil Hate Speech Detection in Migration Discourse",
    author = "Tash, M.  and
      Ahani, Z.  and
      Zamir, M.  and
      Kolesnikova, O.  and
      Sidorov, G.",
    editor = {Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Durairaj, Thenmozhi  and
      Kov{\'a}cs, Gy{\"o}rgy  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel},
    booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = mar,
    year = "2024",
    address = "St. Julian's, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.ltedi-1.20/",
    pages = "184--189",
    abstract = "The exponential rise in social media users has revolutionized information accessibility and exchange. While these platforms serve various purposes, they also harbor negative elements, including hate speech and offensive behavior. Detecting hate speech in diverse languages has garnered significant attention in Natural Language Processing (NLP). This paper delves into hate speech detection in Tamil, particularly related to migration and refuge, contributing to the Caste/migration hate speech detection shared task. Employing a Convolutional Neural Network (CNN), our model achieved an F1 score of 0.76 in identifying hate speech and significant potential in the domain despite encountering complexities. We provide an overview of related research, methodology, and insights into the competition`s diverse performances, showcasing the landscape of hate speech detection nuances in the Tamil language."
}