@inproceedings{subramanian-etal-2025-kec-elite-analysts,
    title = "{KEC}-Elite-Analysts@{LT}-{EDI} 2025: Leveraging Deep Learning for Racial Hoax Detection in Code-Mixed {H}indi-{E}nglish Tweets",
    author = "Subramanian, Malliga  and
      A, Aruna  and
      M, Amudhavan  and
      S, Jahaganapathi  and
      Shanmugavadivel, Kogilavani",
    editor = "Gkirtzou, Katerina  and
      {\v{Z}}itnik, Slavko  and
      Gracia, Jorge  and
      Gromann, Dagmar  and
      di Buono, Maria Pia  and
      Monti, Johanna  and
      Ionov, Maxim",
    booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = sep,
    year = "2025",
    address = "Naples, Italy",
    publisher = "Unior Press",
    url = "https://preview.aclanthology.org/corrections-2025-10/2025.ltedi-1.19/",
    pages = "111--115",
    ISBN = "978-88-6719-334-9",
    abstract = "Detecting misinformation in code-mixed languages, particularly Hindi-English, poses significant challenges in natural language processing due to the linguistic diversity found on social media. This paper focuses on racial hoax detection{---}false narratives that target specific communities{---}within Hindi-English YouTube comments. We evaluate the effectiveness of several machine learning models, including Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Multi-Layer Perceptron, using a dataset of 5,105 annotated comments. Model performance is assessed using accuracy, precision, recall, and F1-score. Experimental results indicate that neural and ensemble models consistently outperform traditional classifiers. Future work will explore the use of transformer-based architectures and data augmentation techniques to enhance detection in low-resource, code-mixed scenarios."
}