  We study the problem of automatic fact-checking, paying special attention to
the impact of contextual and discourse information. We address two related
tasks: (i) detecting check-worthy claims, and (ii) fact-checking claims. We
develop supervised systems based on neural networks, kernel-based support
vector machines, and combinations thereof, which make use of rich input
representations in terms of discourse cues and contextual features. For the
check-worthiness estimation task, we focus on political debates, and we model
the target claim in the context of the full intervention of a participant and
the previous and the following turns in the debate, taking into account
contextual meta information. For the fact-checking task, we focus on answer
verification in a community forum, and we model the veracity of the answer with
respect to the entire question--answer thread in which it occurs as well as
with respect to other related posts from the entire forum. We develop annotated
datasets for both tasks and we run extensive experimental evaluation,
confirming that both types of information ---but especially contextual
features--- play an important role.
