Journal
JOURNAL OF WEB SEMANTICS
Volume 71, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.websem.2021.100663
Keywords
Automated fact-checking; Temporal relevance; Temporal semantics; Document ranking; Learning to rank
Categories
Funding
- European Commission Joint Research Centre [35332]
- COST Action [CA18231]
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The study reveals that considering the temporal information of evidence can improve the veracity predictions of time-sensitive claims. In fact-checking, time-aware evidence ranking surpasses assumptions based purely on semantic similarity or position in a search results list.
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. (C) 2021 The Author(s). Published by Elsevier B.V.
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