Time-Aware Evidence Ranking for Fact-Checking

TL;DR

This paper explores how incorporating temporal information, specifically evidence timestamps, enhances evidence ranking and veracity prediction in fact-checking, especially for claims sensitive to time, by proposing and testing four temporal ranking methods.

Contribution

It introduces four temporal evidence ranking methods that improve fact-checking accuracy by integrating timestamp information into the evidence ranking process.

Findings

Time-aware ranking surpasses semantic similarity-based methods.

Temporal ranking improves veracity prediction for time-sensitive claims.

Evidence timestamps are crucial for accurate fact-checking.

Abstract

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.