# Time-Aware Evidence Ranking for Fact-Checking

arXiv: https://arxiv.org/abs/2009.06402 · HTML: https://mlsanity.com/paper/2009.06402/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Scientific Fact-Checking: A Survey of Resources and Approaches](https://mlsanity.com/paper/2305.16859/) (2023)
- [Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval](https://mlsanity.com/paper/2404.08359/) (2024)
- [Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers](https://mlsanity.com/paper/2205.05435/) (2022)
- [Automatic Fake News Detection: Are Models Learning to Reason?](https://mlsanity.com/paper/2105.07698/) (2021)
- [Implicit Temporal Reasoning for Evidence-Based Fact-Checking](https://mlsanity.com/paper/2302.12569/) (2023)
- [ChronoFact: Timeline-based Temporal Fact Verification](https://mlsanity.com/paper/2410.14964/) (2024)
- [Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection](https://mlsanity.com/paper/2108.13892/) (2021)
- [TSVer: A Benchmark for Fact Verification Against Time-Series Evidence](https://mlsanity.com/paper/2511.01101/) (2025)
- [Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading](https://mlsanity.com/paper/2109.06736/) (2021)
- [When Do Discourse Markers Affect Computational Sentence Understanding?](https://mlsanity.com/paper/2309.00368/) (2023)
- [An Explainable Coherence Score for Detecting Temporal Inconsistencies in Political News](https://mlsanity.com/paper/2608.29175/) (2026)

## Abstract (arXiv)

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.