Language-Aware Truth Assessment of Fact Candidates

Abstract

This paper introduces FactChecker, language-aware approach to truth-finding. FactChecker differs from prior approaches in that it does not rely on iterative peer voting, instead it leverages language to infer believability of fact candidates. In particular, FactChecker makes use of linguistic features to detect if a given source objectively states facts or is speculative and opinionated. To ensure that fact candidates mentioned in similar sources have similar believability, FactChecker augments objectivity with a co-mention score to compute the overall believability score of a fact candidate. Our experiments on various datasets show that FactChecker yields higher accuracy than existing approaches.