Detecting Check-worthy Factual Claims in Presidential Debates

Public figures such as politicians make claims about "facts" all the time. Journalists and citizens spend a good amount of time checking the veracity of such claims. Toward automatic fact checking, we developed tools to find check-worthy factual claims from natural language sentences. Specifically, we prepared a U.S. presidential debate dataset and built classification models to distinguish check-worthy factual claims from non-factual claims and unimportant factual claims. We also identified the most-effective features based on their impact on the classification models' accuracy.