@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://preview.aclanthology.org/landing_page/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."
}