Cited by 71Misinformation and Its ImpactsDeception detection and forensic psychologySpam and Phishing Detection

Fooled by the fakes: Cognitive differences in perceived claim accuracy and sharing intention of non-political deepfakes

Ahmed, Saifuddin·2021·Personality and Individual Differences

Integrity signals

Registry

Crossref · 10.1016/j.paid.2021.111074

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Overall, deepfake detection does not seem to vary based on gender, education, income, or profession.

    “Overall, deepfake detection does not seem to vary based on gender, education, income, or profession [7, 32, 37].”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [32]

  • Cognitive ability and analytical thinking are positively associated with detection accuracy.

    “Cognitive ability and analytical thinking are positively associated with detection accuracy [32, 23].”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [32]

  • Other studies have found no relationship between deepfake detection and age.

    “In contrast, other studies have found no relationship between detection and age [32, 33, 37].”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [32]

  • Providing warnings that a video might be faked is associated with increased accuracy ratings.

    “Providing warnings that a video might be faked is associated with increased accuracy ratings [32].”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [32]

  • Political interest or partisanship has no association with deepfake detection performance across most studies.

    “Overall, political interest and partisanship do not appear to have an impact on detection [23, 32].”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [32]

The citation lists haven’t been fetched yet

References 12 · Cited by 71

Authors
Ahmed, S.
Affiliations
Nanyang Technological University (SG)
Corresponding author
Ahmed, Saifuddin
Venue
Personality and Individual Differences
Publisher
Elsevier BV
Published
2021-11
Volume
182
Pages
111074
ISSN
0191-8869 (ISSN-L)
OpenAlex
W3177228617
Cited by
71
References
12
Citations by year
2026: 14 · 2025: 17 · 2024: 17 · 2023: 14 · 2022: 9
FWCI
13.86
Type
journal article
Language
en
Open access
closed
Locations
  • Personality and Individual Differences (publishedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
Funders
Nanyang Technological University
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords
deepfakes, sharing intention, cognitive ability, individual differences, disinformation, social media influencers, misinformation sharing

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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.

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