@inproceedings{poudhar-etal-2024-strategy,
title = "A Strategy Labelled Dataset of Counterspeech",
author = "Poudhar, Aashima and
Konstas, Ioannis and
Abercrombie, Gavin",
editor = {Chung, Yi-Ling and
Talat, Zeerak and
Nozza, Debora and
Plaza-del-Arco, Flor Miriam and
R{\"o}ttger, Paul and
Mostafazadeh Davani, Aida and
Calabrese, Agostina},
booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.woah-1.20",
doi = "10.18653/v1/2024.woah-1.20",
pages = "256--265",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Strategy Labelled Dataset of Counterspeech
%A Poudhar, Aashima
%A Konstas, Ioannis
%A Abercrombie, Gavin
%Y Chung, Yi-Ling
%Y Talat, Zeerak
%Y Nozza, Debora
%Y Plaza-del-Arco, Flor Miriam
%Y Röttger, Paul
%Y Mostafazadeh Davani, Aida
%Y Calabrese, Agostina
%S Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F poudhar-etal-2024-strategy
%X 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.
%R 10.18653/v1/2024.woah-1.20
%U https://aclanthology.org/2024.woah-1.20
%U https://doi.org/10.18653/v1/2024.woah-1.20
%P 256-265
Markdown (Informal)
[A Strategy Labelled Dataset of Counterspeech](https://aclanthology.org/2024.woah-1.20) (Poudhar et al., WOAH-WS 2024)
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.