The Role of Context in Detecting Previously Fact-Checked Claims

Anonymous

Abstract: Recent years have seen the proliferation of disinformation and fake news online. Traditional proposals to mitigate these problems are manual and 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, where context really matters. 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 more important and can yield 10+points of absolute improvement over a state-of-the-art model.

Paper Type: long