Time-Aware Evidence Ranking for Fact-Checking
This addresses the challenge of handling time-varying truth in fact-checking for applications like misinformation detection, though it is incremental as it builds on existing models.
The paper tackled the problem of fact-checking by incorporating temporal information into evidence ranking, showing that time-aware ranking improves veracity predictions for time-sensitive claims.
Truth can vary over time. Fact-checking decisions on claim veracity should therefore take into account temporal information of both the claim and supporting or refuting evidence. In this work, we investigate the hypothesis that the timestamp of a Web page is crucial to how it should be ranked for a given claim. We delineate four temporal ranking methods that constrain evidence ranking differently and simulate hypothesis-specific evidence rankings given the evidence timestamps as gold standard. Evidence ranking in three fact-checking models is ultimately optimized using a learning-to-rank loss function. Our study reveals that time-aware evidence ranking not only surpasses relevance assumptions based purely on semantic similarity or position in a search results list, but also improves veracity predictions of time-sensitive claims in particular.