HODIAT : a dataset for detecting homotransphobic hate speech in Italian with aggressiveness and target annotation
- Publication type
- C1
- Publication status
- Published
- Authors
- Damo, G., Cignarella, ATC, Caselli, T., Patti, V., & Nozza, D.
- Editor
- Agostina Calabrese, Christine de Kock, Debora Nozza, Flor Miriam Plaza-del-Arco, Zeerak Talat and Francielle Vargas
- Series
- Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH)
- Pagination
- 124-135
- Publisher
- Association for Computational Linguistics (ACL)
- Conference
- 9th Workshop on Online Abuse and Harms (WOAH) (Vienna, Austria)
- Download
- (.pdf)
- View in Biblio
- (externe link)
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.