Time-Aware Evidence Ranking for Fact-Checking
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringFact CheckingLearning-To-RankPositionSemantic SimilaritySemantic Textual SimilaritySimilar Papers 제목 키워드 기반
+VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checking
Identification of appropriate supporting evidence is critical to the success of scientific fact checking. However, existing approaches rely on off-the-shelf Information Retrieval algorithms that rank documents based on r…
Information RetrievalDocument RankingFact CheckingImplicit Temporal Reasoning for Evidence-Based Fact-Checking
Leveraging contextual knowledge has become standard practice in automated claim verification, yet the impact of temporal reasoning has been largely overlooked. Our study demonstrates that time positively influences the c…
Claim VerificationFact CheckingAnswerFact: Fact Checking in Product Question Answering
Product-related question answering platforms nowadays are widely employed in many E-commerce sites, providing a convenient way for potential customers to address their concerns during online shopping. However, the misinf…
Fact CheckingMisinformationQuestion AnsweringAssessing Effectiveness of Using Internal Signals for Check-Worthy Claim Identification in Unlabeled Data for Automated Fact-Checking
While recent work on automated fact-checking has focused mainly on verifying and explaining claims, for which the list of claims is readily available, identifying check-worthy claim sentences from a text remains challeng…
ArticlesFact CheckingRetrievalSemantic Similarity+2Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation
Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not yet be available. Despite increasing in…
ArticlesFact CheckingMisinformation