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- W4287637268 abstract "Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise." @default.
- W4287637268 created "2022-07-25" @default.
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- W4287637268 date "2020-10-19" @default.
- W4287637268 modified "2023-09-28" @default.
- W4287637268 title "Explainable Automated Fact-Checking for Public Health Claims" @default.
- W4287637268 doi "https://doi.org/10.48550/arxiv.2010.09926" @default.
- W4287637268 hasPublicationYear "2020" @default.
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