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Recent years have seen the proliferation of disinformation and fake newsonline. Traditional approaches to mitigate these issues is to use manual orautomatic fact-checking. Recently, another approach has emerged: checkingwhether the input claim has previously been fact-checked, which can be doneautomatically, and thus fast, while also offering credibility andexplainability, thanks to the human fact-checking and explanations in theassociated fact-checking article. Here, we focus on claims made in a politicaldebate and we study the impact of modeling the context of the claim: both onthe source side, i.e., in the debate, as well as on the target side, i.e., inthe fact-checking explanation document. We do this by modeling the localcontext, the global context, as well as by means of co-reference resolution,and multi-hop reasoning over the sentences of the document describing thefact-checked claim. The experimental results show that each of these representsa valuable information source, but that modeling the source-side context ismost important, and can yield 10+ points of absolute improvement over astate-of-the-art model.

The Role of Context in Detecting Previously Fact-Checked Claims