Time-aware evidence ranking for fact-checking

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Standard

Time-aware evidence ranking for fact-checking. / Allein, Liesbeth; Augenstein, Isabelle; Moens, Marie Francine.

In: Journal of Web Semantics, Vol. 71, 100663, 2021.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Allein, L, Augenstein, I & Moens, MF 2021, 'Time-aware evidence ranking for fact-checking', Journal of Web Semantics, vol. 71, 100663. https://doi.org/10.1016/j.websem.2021.100663

APA

Allein, L., Augenstein, I., & Moens, M. F. (2021). Time-aware evidence ranking for fact-checking. Journal of Web Semantics, 71, [100663]. https://doi.org/10.1016/j.websem.2021.100663

Vancouver

Allein L, Augenstein I, Moens MF. Time-aware evidence ranking for fact-checking. Journal of Web Semantics. 2021;71. 100663. https://doi.org/10.1016/j.websem.2021.100663

Author

Allein, Liesbeth ; Augenstein, Isabelle ; Moens, Marie Francine. / Time-aware evidence ranking for fact-checking. In: Journal of Web Semantics. 2021 ; Vol. 71.

Bibtex

@article{a47a161d3a1f460fb78385da48b3973f,
title = "Time-aware evidence ranking for 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.",
keywords = "Automated fact-checking, Document ranking, Learning to rank, Temporal relevance, Temporal semantics",
author = "Liesbeth Allein and Isabelle Augenstein and Moens, {Marie Francine}",
note = "Publisher Copyright: {\textcopyright} 2021 The Author(s)",
year = "2021",
doi = "10.1016/j.websem.2021.100663",
language = "English",
volume = "71",
journal = "Web Semantics",
issn = "1570-8268",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Time-aware evidence ranking for fact-checking

AU - Allein, Liesbeth

AU - Augenstein, Isabelle

AU - Moens, Marie Francine

N1 - Publisher Copyright: © 2021 The Author(s)

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Automated fact-checking

KW - Document ranking

KW - Learning to rank

KW - Temporal relevance

KW - Temporal semantics

UR - http://www.scopus.com/inward/record.url?scp=85117218307&partnerID=8YFLogxK

U2 - 10.1016/j.websem.2021.100663

DO - 10.1016/j.websem.2021.100663

M3 - Journal article

AN - SCOPUS:85117218307

VL - 71

JO - Web Semantics

JF - Web Semantics

SN - 1570-8268

M1 - 100663

ER -

ID: 284630783