Truth can vary over time. Therefore, fact-checking decisions on claim veracity should take into account temporal information of both the claim and supporting or refuting evidence. Automatic fact-checking models typically take claims and evidence pages as input, and previous work has shown that weighing or ranking these evidence pages by their relevance to the claim is useful. However, the temporal information of the evidence pages is not generally considered when defining evidence relevance. In this work, we investigate the hypothesis that the timestamp of an evidence page is crucial to how it should be ranked for a given claim. We delineate four temporal ranking methods that constrain evidence ranking differently: evidence-based recency, claim-based recency, claim-centered closeness and evidence-centered clustering ranking. Subsequently, we simulate hypothesis-specific evidence rankings given the evidence timestamps as gold standard. Evidence ranking is then optimized using a learning to rank loss function. The best performing time-aware fact-checking model outperforms its baseline by up to 33.34%, depending on the domain. Overall, evidence-based recency and evidence-centered clustering ranking lead to the best results. 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.

本研究探讨了时间信息对于事实检验和证据排序的影响，提出了四种不同的证据排序方式，并利用学习排序方法探究了基于时间信息的排名模型在检验真实性时的表现优越性。结果显示，在时间敏感的声明检验中，基于时间信息的排序方法不仅超越了纯语义相似性或搜索结果列表中位置的相关假设，而且提高了真实性预测的效果。

基于时间的事实核查证据排序