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Cognitive Biases in Fact-Checking and Their Countermeasures: A Review

sciencedirect.com 20 September 2026 at 13:08 View original article →

The original link is the material being checked. It may contain false or misleading claims — we link for transparency, not endorsement.

93/100
Verdict High Trust Highly Reliable — Peer-reviewed open-access scholarly record page (metadata verified); dynamic platform metrics unverified

The submitted content is a scraped ScienceDirect landing page for the peer-reviewed review article 'Cognitive Biases in Fact-Checking and Their Countermeasures: A Review' by Soprano, Roitero, La Barbera, Ceolin, Spina, Demartini and Mizzaro, published in Elsevier's Information Processing & Management (2024), article 103672, DOI 10.1016/j.ipm.2024.103672.

13 checks out 0 disputed ? 6 unverified
Full analysis The complete summary

The submitted content is a scraped ScienceDirect landing page for the peer-reviewed review article 'Cognitive Biases in Fact-Checking and Their Countermeasures: A Review' by Soprano, Roitero, La Barbera, Ceolin, Spina, Demartini and Mizzaro, published in Elsevier's Information Processing & Management (2024), article 103672, DOI 10.1016/j.ipm.2024.103672. All core bibliographic and substantive claims — authorship, journal, article number and DOI, open-access Creative Commons status, the PRISMA-inspired method, the derivation of 221 cognitive biases, the selection of 39 relevant biases, the 11 proposed countermeasures and the bias-aware fact-checking pipeline — were independently corroborated against the publisher record, the ACM Digital Library entry, the CWI institutional repository full text, dblp, and author/institutional pages. Two residual issues prevent a higher score: (i) the PlumX-style metrics reproduced on the page (50 citation indexes, 120 Mendeley readers, 1 policy citation, 2 news mentions, 11 social shares) are time-variable platform counts with no verifiable snapshot and are therefore Unverified — the ACM entry independently reports a different, platform-specific citation count; and (ii) a minor metadata discrepancy exists between indexes, with dblp listing the article in volume 61, issue 2 rather than issue 3. The text is a publisher interface dump containing navigation chrome, cookie-consent boilerplate and stripped hyperlinks, so it carries no persuasive framing; its emotional loading is negligible and its high self-reference count reflects ordinary academic 'we' usage in an abstract and highlights list, not promotional rhetoric.

What checked out (13)
  • The article is titled 'Cognitive Biases in Fact-Checking and Their Countermeasures: A Review' and is published in Information Processing & Management, article number 103672 (2024) — confirmed on the publisher page (ScienceDirect, PII S0306457324000323) and the ACM Digital Library record.
  • The DOI is 10.1016/j.ipm.2024.103672 — confirmed by ACM Digital Library, the CWI institutional listing for Davide Ceolin and the University of Queensland expert profile for Gianluca Demartini.
  • The author list is Michael Soprano, Kevin Roitero, David La Barbera, Davide Ceolin, Damiano Spina, Gianluca Demartini and Stefano Mizzaro, in that order — confirmed by ScienceDirect, ACM, dblp and the CWI full-text PDF.
  • Author affiliations correspond to the a/b/c/d markers on the page: University of Udine (a), Centrum Wiskunde & Informatica, Amsterdam (b), RMIT University, Melbourne (c) and The University of Queensland (d) — confirmed by the CWI repository full text.
  • The article is open access under a Creative Commons licence and published by Elsevier Ltd; the full text states it is an open access article under the CC BY licence, © 2024 The Authors.
  • The stated method is inspired by PRISMA, a methodology used for systematic literature reviews — confirmed in the abstract as reproduced by ACM and by the publisher page.
  • The claim that the authors manually derive a list of 221 cognitive biases that may affect human assessors is accurate — confirmed by the ACM abstract and by the publisher's listing of 'Appendix B. List of 221 cognitive biases'.
  • The highlight claiming identification of 39 cognitive biases that may compromise the fact-checking process is accurate — confirmed verbatim on the ScienceDirect highlights and the ACM record.
  • The claim of a set of 11 countermeasures to mitigate cognitive biases in fact-checking is accurate — confirmed on the ACM record and the author's institutional publication page.
  • The highlight describing the constituting elements of a bias-aware fact-checking pipeline is accurate — confirmed on the ACM record and the publisher highlights.
  • The journal designation 'Volume 61, Issue 3' with a May 2024 date is supported by the publisher page, by the ACM record's 'Vol 61, No 3' and 1 May 2024 publication date, and by the CWI bibliographic listing (61(3), 103672:1–103672:29).
  • The keywords listed (cognitive bias; misinformation; fact-checking; truthfulness assessment) are consistent with the published abstract and subject matter on the publisher page.
  • The abstract text reproduced on the page matches the published abstract wording available from independent mirrors (ACM Digital Library and the CWI open repository full text).
? Unverified claims 6 claims
  • PlumX-style metric 'Citation Indexes 50' — cannot be verified as a fixed value; it is a live, continuously updated aggregate with no supplied timestamp. ACM independently reported a much lower platform-specific total (6 citations), which is expected because counting bases differ, but this prevents confirmation of the figure as stated.
  • PlumX metric 'Mendeley Readers 120' — a dynamic capture count with no snapshot date; not independently confirmable.
  • PlumX metrics 'Policy Citations 1', 'Blog Mentions 1', 'News Mentions 2', 'References 3' and 'Shares, Likes & Comments 11' — dynamic altmetric counts, no date stamp, no accessible primary PlumX snapshot; Unverified.
  • The copyright footer 'Copyright © 2026 Elsevier B.V.' — an automatically generated site-wide notice reflecting the scrape date rather than a claim about the article; treated as interface boilerplate, not a factual assertion.
  • The Data availability statement 'All the data involved have been provided in the appendices' — plausible and consistent with the publisher's listing of Appendices A and B, but completeness of the underlying data could not be independently audited within this review.
  • Volume/issue consistency across indexes — dblp records the article as Inf. Process. Manag. 61(2): 103672 (2024) against the publisher's and ACM's 61(3). The publisher record is authoritative, so the page's '61(3)' is treated as correct, but the discrepancy is flagged as an unresolved third-party indexing inconsistency rather than an error in the content.
Sources & how we checked Search journal, source grades, confidence
Confidence

High — Confidence is high because every high-priority, falsifiable claim on the page was corroborated by the primary publisher record and by at least two further independent sources (ACM Digital Library, dblp, CWI repository full text, and institutional author profiles at CWI, RMIT and UQ), with no contradictions among them. The content type — a DOI-registered, peer-reviewed, open-access article record — is inherently stable and auditable, which limits the scope for undetected error. Confidence is not rated Very High for three reasons: the six PlumX-style altmetric figures are undated and could not be reconciled with any accessible snapshot, so they remain Unverified; dblp's volume/issue entry diverges from the publisher's, indicating that at least one major index carries incorrect metadata; and the submitted capture had all hyperlinks stripped, preventing assessment of the page's own outbound provenance and of funding or competing-interest declarations. None of these residual uncertainties bears on the substantive accuracy of the article's described scope, method or findings.

Search journal

Cognitive Biases in Fact-Checking and Their Countermeasures: A Review Soprano Roitero

10.1016/j.ipm.2024.103672

Information Processing & Management Volume 61 Issue 3 103672 cognitive biases fact-checking

Soprano Roitero La Barbera Ceolin Spina Demartini Mizzaro 39 cognitive biases 11 countermeasures

"11 countermeasures" cognitive biases fact-checking bias-aware pipeline Soprano 2024

Soprano 2024 cognitive biases fact-checking citations Semantic Scholar

Michael Soprano University of Udine Davide Ceolin CWI Damiano Spina RMIT affiliations cognitive biases review

Article metrics

Emotion 8% · Reading grade 14.2 · 973 words

The article we checked Full text as retrieved
[Skip to main content]( to article]( []( * [Journals & Books]( * Help * [Search]( [My account]( [Sign in]( []( ## [Information Processing & Management]( "Go to Information Processing & Management on ScienceDirect") Date:May 2024 Article:103672 Volume:[Volume 61, Issue 3]( "Go to table of contents for this volume/issue") View accessibility information ## Published by:Elsevier ### Published by []( "Go to Information Processing & Management on ScienceDirect") Show more Research article Open access Under a Creative Commons [license]( [Get rights and content]( # Cognitive Biases in Fact-Checking and Their Countermeasures: A Review Author links open overlay panel Michael Soprano a, [Kevin Roitero a]( David La Barbera a, Davide Ceolin b, [Damiano Spina c]( [Gianluca Demartini d]( [Stefano Mizzaro a]( Show more [View**PDF**]( full issue Cite Add to Mendeley Share [10.1016/j.ipm.2024.103672]( More actions * [Article]( * [Metrics]( ## Highlights * •We identify 39 cognitive biases that may compromise the fact-checking process. * •Through a systematic review, we highlight key categories of cognitive biases influencing human assessors. * •We propose a set of 11 countermeasures to mitigate the impact of cognitive biases on fact-checking activities. * •We describe the constituting elements of a bias-aware fact-checking pipeline. ## Abstract The increase of the amount of misinformation spread every day online is a huge threat to the society. Organizations and researchers are working to contrast this misinformation plague. In this setting, human assessors are indispensable to correctly identify, assess and/or revise the truthfulness of information items, i.e., to perform the fact-checking activity. Assessors, as humans, are subject to systematic errors that might interfere with their fact-checking activity. Among such errors, cognitive biases are those due to the limits of human cognition. Although biases help to minimize the cost of making mistakes, they skew assessments away from an objective perception of information. Cognitive biases, hence, are particularly frequent and critical, and can cause errors that have a huge potential impact as they propagate not only in the community, but also in the datasets used to train automatic and semi-automatic machine learning models to fight misinformation. In this work, we present a review of the cognitive biases which might occur during the fact-checking process. In more detail, inspired by PRISMA – a methodology used for systematic literature reviews – we manually derive a list of 221 cognitive biases that may affect human assessors. Then, we select the 39 biases that might manifest during the fact-checking process, we group them into categories, and we provide a description. Finally, we present a list of 11 countermeasures that can be adopted by researchers, practitioners, and organizations to limit the effect of the identified cognitive biases on the fact-checking activity. ## Keywords Cognitive bias ; Misinformation ; Fact-checking ; Truthfulness assessment * [Previous article in this issue]( * [Next article in this issue]( Sorry, something went wrong. Please try again and make sure cookies are enabled ## Data availability All the data involved have been provided in the appendices. ## Recommended articles Recommended articles cannot be displayed at this time. ## Metrics ### Citations * Citation Indexes 50 * Policy Citations 1 ### Captures * Mendeley Readers 120 ### Mentions * Blog Mentions 1 * News Mentions 2 * References 3 ### Social Media * Shares, Likes & Comments 11 [View details]( © 2024 The Authors. Published by Elsevier Ltd. []( * [About ScienceDirect]( * [Remote access]( * [Contact and support]( * [Terms and conditions]( * [Privacy policy]( * Cookie settings All content on this site: Copyright © 2026 Elsevier B.V., its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply. 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