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Prior exposure increases perceived accuracy of fake news
Open accessAbstract
The 2016 U.S. presidential election brought considerable attention to the phenomenon of "fake news": entirely fabricated and often partisan content that is presented as factual. Here we demonstrate one mechanism that contributes to the believability of fake news: fluency via prior exposure. Using actual fake-news headlines presented as they were seen on Facebook, we show that even a single exposure increases subsequent perceptions of accuracy, both within the same session and after a week. Moreover, this "illusory truth effect" for fake-news headlines occurs despite a low level of overall believability and even when the stories are labeled as contested by fact checkers or are inconsistent with the reader's political ideology. These results suggest that social media platforms help to incubate belief in blatantly false news stories and that tagging such stories as disputed is not an effective solution to this problem. It is interesting, however, that we also found that prior exposure does not impact entirely implausible statements (e.g., "The earth is a perfect square"). These observations indicate that although extreme implausibility is a boundary condition of the illusory truth effect, only a small degree of potential plausibility is sufficient for repetition to increase perceived accuracy. As a consequence, the scope and impact of repetition on beliefs is greater than has been previously assumed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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“Reducing the sharing of misinformation is of substantial importance, because simply being exposed to misinformation can increase subsequent belief13,20–22.”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [13]
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“Some explanations for belief in misinformation identify factors that resist intervention (4), while others propose policy approaches that are effective in practice but difficult to scale (34, 44).”
From A digital media literacy intervention increases discernment between mainstream and false news in the United States and India (2020) · cites this paper as [44]
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“46 Another study set in a simulated social media environment found that people were more likely to share false content after being previously exposed to it.”
Bowes, S. M., & Fazio, L. K. (2026) · Psychonomic Bulletin & Review · doi:10.3758/s13423-025-02836-w · Cited by 2
Manipulating Minds: The Psychological Underpinnings of Misinformation
Delouvée, S., & Wagner‐Egger, P. (2026) · Springer eBooks · doi:10.1007/978-3-032-00480-2_3 · Cited by 1
Mason, C. E., & Rapp, D. N. (2026) · Applied Cognitive Psychology · doi:10.1002/acp.70166 · Cited by 1
Polizzi, G., Harrison, T., & McLoughlin, S. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2616869 · Cited by 1
Lois, G., Gardikiotis, A., Karaspyrou, Z., Tsakanikos, E., & Sedikides, C. (2026) · Political Psychology · doi:10.1111/pops.70109 · Cited by 1
“…, encouraging individuals prone to intuitive thinking to rely on heuristics such as familiarity, fluency, likability, or source credibility, which can produce an illusory sense of truth or plausibility (Kim & Lee, 2025; Pennycook et al., 2018; Pennycook & Rand, 2020, 2021; Swire et al., 2017).”
Seeing is Believing? Understanding Truth in Synthetic Media
Totti, C., Cavicchioli, R., Furini, M., & Tagliani, G. (2026) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc65079.2026.11366358 · Cited by 1
“On the cognitive side, mechanisms such as the illusory truth effect [9] and visual priming [1] reinforce the perceived credibility of false claims, especially when images evoke strong symbolic or emotional associations [12].”
Götzfried, A., & Heitmayer, M. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100255 · Cited by 5
Ontological (in)security after truth: Disinformation as affective technology
Bilgiç, A. (2026) · Cooperation and Conflict · doi:10.1177/00108367261422466 · Cited by 5
Hameleers, M., van der Meer, T. G. L. A., Tulin, M., & Dobber, T. (2026) · Communication Research · doi:10.1177/00936502261421437 · Cited by 7
Flagged but Fooled? Rethinking Misinformation Inoculation on Short-Form Video Apps
Jung, Y. (2026) · Cyberpsychology Behavior and Social Networking · doi:10.1177/21522715261432858 · Cited by 1
“Prior research indicates that warnings are often effective only after repeated exposure, 28 and meta-analyses have shown that inoculation treatments require time to build meaningful resistance to misinformation.”
Time to (Re)visualise victims? Introducing shadow victims of digital misinformation
Mukherjee, S., & Chang, L. Y. C. (2026) · Computers in Human Behavior · doi:10.1016/j.chb.2026.109002 · Cited by 1
Ghenai, A., Wang, X., & Moffatt, K. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101033 · Cited by 1
Deepfaking the past: Memory and perceived truth of resurrected historical figures
Soto-Sanfiel, M. T., & Fu, G. J. (2026) · Computers in Human Behavior · doi:10.1016/j.chb.2026.109008 · Cited by 2
Fox, G., van der Werff, L., Santos, G., Egli, A., Wood, C., & Lynn, T. (2026) · Journal of Health Population and Nutrition · doi:10.1186/s41043-026-01321-x · Cited by 1
“Consumers’ expectations of lower accuracy in social media contexts [43, 44] and limited effort in processing information [45] may further increase the likelihood of consuming misinformation, and recent evidence suggests health misinformation may be harder to detect than misinformation in other…”
Khatiwada, P., Syed, N., Halko, L., Mahesh, A., Kiamilev, R., Vazquez, A., & Mauriello, M. L. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2664696 · Cited by 1
From passive consumers to critical evaluators: promoting media literacy in animal science
Bowhay, C. M. (2026) · Translational Animal Science · doi:10.1093/tas/txag073 · Cited by 1
“This is likely due in part to the illusory truth effect (Vasu et al. 2018), where the same inaccurate information repeated from multiple sources creates the illusion of credibility (Pennycook et al. 2018).”
Metadata
- Authors
- Pennycook, G., Cannon, T. D., & Rand, D. G.
- Affiliations
- Yale University (US)
- Venue
- Journal of Experimental Psychology General
- Published
- 2018-12
- Volume
- 147
- Issue
- 12
- Pages
- 1865-1880
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- PMC
- PMC6279465
- OpenAlex
- W3125618971
- Cited by
- 1,268
- References
- 58
- Citations by year
- 2026: 117 · 2025: 139 · 2024: 141 · 2023: 204 · 2022: 188 · 2021: 198 · 2020: 180 · 2019: 79 · 2018: 20
- FWCI
- 270.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Experimental Psychology General (publishedVersion, other-oa)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- Social Sciences and Humanities Research Council of Canada
- National Institute of Mental Health (MH081902)
- Templeton World Charity Foundation
- Defense Advanced Research Projects Agency
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Keywords
- fake news, illusory truth effect, prior exposure, perceived accuracy, fluency, social media, Facebook, 2016 US presidential election, fact-checking, political ideology, belief formation, repetition
- MeSH terms
- Adult, Female, Humans, Judgment, Deception, Male, Perception, Politics, United States, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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