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HODIAT : a dataset for detecting homotransphobic hate speech in Italian with aggressiveness and target annotation

Greta Damo, Alessandra Teresa Cignarella (UGent) , Tommaso Caselli, Viviana Patti and Debora Nozza
(2025) Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH). p.124-135
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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.

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MLA
Damo, Greta, et al. “HODIAT : A Dataset for Detecting Homotransphobic Hate Speech in Italian with Aggressiveness and Target Annotation.” Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH), edited by Agostina Calabrese et al., Association for Computational Linguistics (ACL), 2025, pp. 124–35.
APA
Damo, G., Cignarella, A. T., Caselli, T., Patti, V., & Nozza, D. (2025). HODIAT : a dataset for detecting homotransphobic hate speech in Italian with aggressiveness and target annotation. In A. Calabrese, C. de Kock, D. Nozza, F. Miriam Plaza-del-Arco, Z. Talat, & F. Vargas (Eds.), Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH) (pp. 124–135). Association for Computational Linguistics (ACL).
Chicago author-date
Damo, Greta, Alessandra Teresa Cignarella, Tommaso Caselli, Viviana Patti, and Debora Nozza. 2025. “HODIAT : A Dataset for Detecting Homotransphobic Hate Speech in Italian with Aggressiveness and Target Annotation.” In Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH), edited by Agostina Calabrese, Christine de Kock, Debora Nozza, Flor Miriam Plaza-del-Arco, Zeerak Talat, and Francielle Vargas, 124–35. Association for Computational Linguistics (ACL).
Chicago author-date (all authors)
Damo, Greta, Alessandra Teresa Cignarella, Tommaso Caselli, Viviana Patti, and Debora Nozza. 2025. “HODIAT : A Dataset for Detecting Homotransphobic Hate Speech in Italian with Aggressiveness and Target Annotation.” In Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH), ed by. Agostina Calabrese, Christine de Kock, Debora Nozza, Flor Miriam Plaza-del-Arco, Zeerak Talat, and Francielle Vargas, 124–135. Association for Computational Linguistics (ACL).
Vancouver
1.
Damo G, Cignarella AT, Caselli T, Patti V, Nozza D. HODIAT : a dataset for detecting homotransphobic hate speech in Italian with aggressiveness and target annotation. In: Calabrese A, de Kock C, Nozza D, Miriam Plaza-del-Arco F, Talat Z, Vargas F, editors. Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH). Association for Computational Linguistics (ACL); 2025. p. 124–35.
IEEE
[1]
G. Damo, A. T. Cignarella, T. Caselli, V. Patti, and D. Nozza, “HODIAT : a dataset for detecting homotransphobic hate speech in Italian with aggressiveness and target annotation,” in Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH), Vienna, Austria, 2025, pp. 124–135.
@inproceedings{01KAJSW2ZFADR1CKS2X2GT7ZX3,
  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.}},
  author       = {{Damo, Greta and Cignarella, Alessandra Teresa and Caselli, Tommaso and Patti, Viviana and Nozza, Debora}},
  booktitle    = {{Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH)}},
  editor       = {{Calabrese, Agostina and de Kock, Christine and Nozza, Debora and Miriam Plaza-del-Arco, Flor and Talat, Zeerak and Vargas, Francielle}},
  isbn         = {{9798891761056}},
  language     = {{eng}},
  location     = {{Vienna, Austria}},
  pages        = {{124--135}},
  publisher    = {{Association for Computational Linguistics (ACL)}},
  title        = {{HODIAT : a dataset for detecting homotransphobic hate speech in Italian with aggressiveness and target annotation}},
  url          = {{https://aclanthology.org/2025.woah-1.11/}},
  year         = {{2025}},
}