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        <title>The Role of Context in Detecting Previously Fact-Checked Claims</title>
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    <abstract>Recent years have seen the proliferation of disinformation and fake news online. Traditional approaches to mitigate these issues is to use manual or automatic fact-checking. Recently, another approach has emerged: checking whether the input claim has previously been fact-checked, which can be done automatically, and thus fast, while also offering credibility and explainability, thanks to the human fact-checking and explanations in the associated fact-checking article. Here, we focus on claims made in a political debate and we study the impact of modeling the context of the claim: both on the source side, i.e., in the debate, as well as on the target side, i.e., in the fact-checking explanation document. We do this by modeling the local context, the global context, as well as by means of co-reference resolution, and multi-hop reasoning over the sentences of the document describing the fact-checked claim. The experimental results show that each of these represents a valuable information source, but that modeling the source-side context is most important, and can yield 10+ points of absolute improvement over a state-of-the-art model.</abstract>
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%0 Conference Proceedings
%T The Role of Context in Detecting Previously Fact-Checked Claims
%A Shaar, Shaden
%A Alam, Firoj
%A Da San Martino, Giovanni
%A Nakov, Preslav
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F shaar-etal-2022-role
%X Recent years have seen the proliferation of disinformation and fake news online. Traditional approaches to mitigate these issues is to use manual or automatic fact-checking. Recently, another approach has emerged: checking whether the input claim has previously been fact-checked, which can be done automatically, and thus fast, while also offering credibility and explainability, thanks to the human fact-checking and explanations in the associated fact-checking article. Here, we focus on claims made in a political debate and we study the impact of modeling the context of the claim: both on the source side, i.e., in the debate, as well as on the target side, i.e., in the fact-checking explanation document. We do this by modeling the local context, the global context, as well as by means of co-reference resolution, and multi-hop reasoning over the sentences of the document describing the fact-checked claim. The experimental results show that each of these represents a valuable information source, but that modeling the source-side context is most important, and can yield 10+ points of absolute improvement over a state-of-the-art model.
%R 10.18653/v1/2022.findings-naacl.122
%U https://aclanthology.org/2022.findings-naacl.122
%U https://doi.org/10.18653/v1/2022.findings-naacl.122
%P 1619-1631

Markdown (Informal)

[The Role of Context in Detecting Previously Fact-Checked Claims](https://aclanthology.org/2022.findings-naacl.122) (Shaar et al., Findings 2022)

• The Role of Context in Detecting Previously Fact-Checked Claims (Shaar et al., Findings 2022)

ACL

• Shaden Shaar, Firoj Alam, Giovanni Da San Martino, and Preslav Nakov. 2022. The Role of Context in Detecting Previously Fact-Checked Claims. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1619–1631, Seattle, United States. Association for Computational Linguistics.