Claim Check-Worthiness Detection as Positive Unlabelled Learning


A critical component of automatically combating misinformation is the detection of fact check-worthiness, i.e. determining if a piece of information should be checked for veracity. There are multiple isolated lines of research which address this core issue: check-worthiness detection from political speeches and debates, rumour detection on Twitter, and citation needed detection from Wikipedia. What is still lacking is a structured comparison of these variants of check-worthiness, as well as a unified approach to them. We find that check-worthiness detection is a very challenging task in any domain, because it both hinges upon detecting how factual a sentence is, and how likely a sentence is to be believed without verification. As such, annotators often only mark those instances they judge to be clear-cut check-worthy. Our best-performing method automatically corrects for this, using a variant of positive unlabelled learning, which learns when an instance annotated as not check-worthy should in fact have been annotated as being check-worthy. In applying this, we outperform the state of the art in two of the three domains studied for check-worthiness detection in English.

In Findings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)