  In the context of investigative journalism, we address the problem of
automatically identifying which claims in a given document are most worthy and
should be prioritized for fact-checking. Despite its importance, this is a
relatively understudied problem. Thus, we create a new dataset of political
debates, containing statements that have been fact-checked by nine reputable
sources, and we train machine learning models to predict which claims should be
prioritized for fact-checking, i.e., we model the problem as a ranking task.
Unlike previous work, which has looked primarily at sentences in isolation, in
this 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 the
public. Our experiments show state-of-the-art results, outperforming a strong
rivaling system by a margin, while also confirming the importance of the
contextual information.
