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CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/P19-1271.pdf

Summary

Online hate is usually handled by deleting posts or suspending accounts, which can be criticized as censorship. The authors focus instead on counter-narratives: fact-based, non-aggressive replies that can calm a discussion and challenge stereotypes. No suitable large collection of such replies existed. The one earlier Twitter-based effort was English-only, limited to a fixed template, and losing data as tweets disappeared. Many of its replies were also rude. The authors therefore built CONAN through nichesourcing, which means recruiting trained specialists rather than anonymous crowd workers. Over 100 operators from three NGOs in the UK, France and Italy wrote pairs of hate speech and counter-narratives about Islamophobia. They did this in simulated social-media sessions, which also protects their identities. Non-expert annotators then added paraphrases, translations into English, and labels for hate sub-topic and response type. Author demographics are included as well. Human evaluations show three results:

  • Paraphrased pairs stay nearly as coherent as the originals.
  • Paraphrases improve a simple tf-idf reply-retrieval baseline.
  • Readers prefer replies written by operators of their own gender.

The resource, released as plain text, lays groundwork for automatic counter-narrative generation to assist NGOs.

Key points

  • Dataset size and scope: CONAN contains 4078 hate speech/counter-narrative pairs in English, French and Italian, all about Islamophobia. It is available at github.com/marcoguerini/CONAN.
  • Collection effort: Expert writing took more than 500 person-hours. The NGOs were Stop Hate UK, Collectif Contre l'Islamophobie en France, and Amnesty International's Italian section. Three non-expert annotators added about 200 more person-hours.
  • Paraphrase quality: In the pairing-quality test, evaluators judged pairs as clearly linked in these proportions:
    • 85% for original pairs
    • 74% for paraphrased-hate pairs
    • 4% for randomly matched pairs
  • Retrieval gain: Adding two paraphrases per hate message to a tf-idf retrieval model raised "very relevant" answers for 100 real tweets from 18% to 27% (p < .01).
  • Gender effect: Annotators would reuse a reply 47% of the time when they shared the author's gender, compared with 32% otherwise (p < .001). The work was partly funded by the EU HATEMETER project.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

114 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 111 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/p19-1271

@inproceedings{chung2019conan,
  title = {{CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech}},
  author = {Chung, Yi-Ling and Kuzmenko, Elizaveta and Tekiroglu, Serra Sinem and Guerini, Marco},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1271},
  url = {https://doi.org/10.18653/v1/p19-1271}
}

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