Abstract
Increasing hateful conduct online demands effective counterspeech strategies to mitigate its impact. We introduce a novel dataset annotated with such strategies, aimed at facilitating the generation of targeted responses to hateful language. We labelled 1000 hate speech/counterspeech pairs from an existing dataset with strategies established in the social sciences. We find that a one-shot prompted classification model achieves promising accuracy in classifying the strategies according to the manual labels, demonstrating the potential of generative Large Language Models (LLMs) to distinguish between counterspeech strategies.- Anthology ID:
- 2024.woah-1.20
- Volume:
- Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Yi-Ling Chung, Zeerak Talat, Debora Nozza, Flor Miriam Plaza-del-Arco, Paul Röttger, Aida Mostafazadeh Davani, Agostina Calabrese
- Venues:
- WOAH | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 256–265
- Language:
- URL:
- https://aclanthology.org/2024.woah-1.20
- DOI:
- Cite (ACL):
- Aashima Poudhar, Ioannis Konstas, and Gavin Abercrombie. 2024. A Strategy Labelled Dataset of Counterspeech. In Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024), pages 256–265, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- A Strategy Labelled Dataset of Counterspeech (Poudhar et al., WOAH-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.woah-1.20.pdf