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In the context of investigative journalism, we address the problem ofautomatically identifying which claims in a given document are most worthy andshould be prioritized for fact-checking. Despite its importance, this is arelatively understudied problem. Thus, we create a new dataset of politicaldebates, containing statements that have been fact-checked by nine reputablesources, and we train machine learning models to predict which claims should beprioritized for fact-checking, i.e., we model the problem as a ranking task.Unlike previous work, which has looked primarily at sentences in isolation, inthis paper we focus on a rich input representation modeling the context:relationship between the target statement and the larger context of the debate,interaction between the opponents, and reaction by the moderator and by thepublic. Our experiments show state-of-the-art results, outperforming a strongrivaling system by a margin, while also confirming the importance of thecontextual information.

A Context-Aware Approach for Detecting Check-Worthy Claims in Political Debates