Automatic Fact-Checking Using Context and Discourse Information

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.Comment: JDIQ,Special Issue on Combating Digital Misinformation and Disinformatio

Overview of the NTCIR-8 Community QA Pilot Task (Part I): The Test Collection and the Task

Identifying high-quality content in community-type Q&A (CQA) sites is important. We propose a task in which a computer identifies good answers from such sites. We describe the design of our bestanswer estimation task using Yahoo! Chiebukuro. We also describe a method of constructing the test collection used for our CQA pilot task, the manual assessment method, and assessment results