@inproceedings{damo-etal-2025-hodiat,
    title = "{HODIAT}: A Dataset for Detecting Homotransphobic Hate Speech in {I}talian with Aggressiveness and Target Annotation",
    author = "Damo, Greta  and
      Cignarella, Alessandra Teresa  and
      Caselli, Tommaso  and
      Patti, Viviana  and
      Nozza, Debora",
    editor = "Calabrese, Agostina  and
      de Kock, Christine  and
      Nozza, Debora  and
      Plaza-del-Arco, Flor Miriam  and
      Talat, Zeerak  and
      Vargas, Francielle",
    booktitle = "Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.woah-1.11/",
    pages = "124--135",
    ISBN = "979-8-89176-105-6",
    abstract = "The escalating spread of homophobic and transphobic rhetoric in both online and offline spaces has become a growing global concern, with Italy standing out as one of the countries where acts of violence against LGBTQIA+ individuals persist and increase year after year. This short paper study analyzes hateful language against LGBTQIA+ individuals in Italian using novel annotation labels for aggressiveness and target. We assess a range of multilingual and Italian language models on this newannotation layers across zero-shot, few-shot, and fine-tuning settings. The results reveal significant performance gaps across models and settings, highlighting the limitations of zero- and few-shot approaches and the importance of fine-tuning on labelled data, when available, to achieve high prediction performance."
}