The Role of Context in Detecting Previously Fact-Checked Claims

TL;DR

This paper investigates how contextual information from debates and fact-checking documents improves automatic detection of previously fact-checked claims, emphasizing source-side context for significant accuracy gains.

Contribution

It introduces a comprehensive approach to model local, global, and co-reference context in claim verification, highlighting the importance of source-side context for improved performance.

Findings

Modeling source context yields over 10 points accuracy improvement.

Contextual modeling enhances fact-checking claim detection.

Source-side context is more impactful than other context types.

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.