@inproceedings{achamaleh-etal-2025-em,
    title = "{EM}-26@{LT}-{EDI} 2025: Detecting Racial Hoaxes in Code-Mixed Social Media Data",
    author = "Achamaleh, Tewodros  and
      Uroosa, Fatima  and
      Hafeez, Nida  and
      Abiola, Tolulope Olalekan  and
      Mebraihtu, Mikiyas  and
      Getachew, Sara  and
      Sidorov, Grigori  and
      Quintero, Rolando",
    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/add-orcids-2024-emnlp/2025.ltedi-1.25/",
    pages = "146--152",
    ISBN = "978-88-6719-334-9",
    abstract = "Social media platforms and user-generated content, such as tweets, comments, and blog posts often contain offensive language, including racial hate speech, personal attacks, and sexual harassment. Detecting such inappropriate language is essential to ensure user safety and to prevent the spread of hateful behavior and online aggression. Approaches base on conventional machine learning and deep learning have shown robust results for high-resource languages like English and find it hard to deal with code-mixed text, which is common in bilingual communication. We participated in the shared task ``LT-EDI@LDK 2025'' organized by DravidianLangTech, applying the BERT-base multilingual cased model and achieving an F1 score of 0.63. These results demonstrate how our model effectively processes and interprets the unique linguistic features of code-mixed content. The source code is available on GitHub.1"
}