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Disinformation in Science

A Review of Empirical Evidence on Causes, Diffusion, and Countermeasures

Bibliographic Data

ID19216445
AuthorsJakub Lysek (0000-0003-3583-9917, Palacký University Olomouc), PAVEL ŠARADÍN (0000-0003-0470-5430, Palacký University Olomouc), Markéta Zapletalová (0000-0003-2645-8944, Palacký University Olomouc), Tomáš Lebeda (0000-0002-8931-9274, Palacký University Olomouc), Eva Lebedová (0000-0002-2433-8677, Palacký University Olomouc), Daniela Ostrá (0000-0002-0778-3242, Palacký University Olomouc), Ondrej MOLNÁR (0000-0001-9646-3680, Palacký University Olomouc)
Year2026
Publication date2026-06-12
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCzech Sociological Review (JOURNAL)
Journal identifiersISSN: 0038-0288 • E-ISSN: 2336-128X
PublisherInstitute of Sociology of the Czech Academy of Sciences (PUBLISHER • CZ)
DOI10.13060/csr.2026.021
OpenAlexW7164586214
LanguageEN
References cited66

This article presents a synthesis of empirical quantitative findings on the individual-level factors that influence the susceptibility to believe scientific and technological disinformation and the willingness to disseminate it.The review identifies determinants divided into three main categories: socioeconomic status (SES), attitudinal and value orientations, and psychological factors.Key findings indicate that the influence of formal education and economic capital is often moderated, or even overshadowed, by ideological orientation and deeper personality and psychological traits.The intentional dissemination of disinformation is strongly associated with two factors in particular that are part of psychology's Dark Triad theory: narcissism and Machiavellianism.Conversely, cognitive reflection (CRT) emerges as the strongest protective factor.Consequently, effective defence strategies must move away from simplistic fact-checking and focus on developing psychological resilience, metacognition, and critical thinking in recipients.Future research should shift from predominantly focusing on respondents via additive regression modelling towards investigating interaction and moderation effects and thematically towards examining the role of political and business elites as drivers of disinformation narratives

Disinformation · Empirical evidence · Government (linguistics) · Misinformation · Benford’s Law and Fraud Detection · Media Influence and Politics · Misinformation and Its Impacts

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