Automated Fact Checking in the News Room
This addresses the problem of misinformation for journalists, but it is incremental as it builds on existing methods with modest performance gains.
The paper tackles automated fact-checking by developing a platform that retrieves evidence, predicts support or refutation, and provides a verdict, achieving 58% correct predictions and 59% relevant evidence in a user study with journalists.
Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation. In this paper, we present an automated fact-checking platform which given a claim, it retrieves relevant textual evidence from a document collection, predicts whether each piece of evidence supports or refutes the claim, and returns a final verdict. We describe the architecture of the system and the user interface, focusing on the choices made to improve its user-friendliness and transparency. We conduct a user study of the fact-checking platform in a journalistic setting: we integrated it with a collection of news articles and provide an evaluation of the platform using feedback from journalists in their workflow. We found that the predictions of our platform were correct 58\% of the time, and 59\% of the returned evidence was relevant.