An error management approach to perceived fakeness of deepfakes: The moderating role of perceived deepfake targeted politicians’ personality characteristics
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
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 [33]
Participants were more likely to categorize real videos as fake when they viewed the depicted subjects (Trump and Obama) as dangerous; the opposite was observed for trust-participants who trusted the depicted politician were less likely to categorize the deepfake video as fake.
“Participants were more likely to categorize the real videos as fake when they viewed the depicted subjects (in this case, Trump and Obama) as dangerous. The opposite is observed for trust - participants who trusted the depicted politician were less likely to categorize the deepfake video as fake [33].”
From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [33]
Participants rated videos of Donald Trump as more fake (4.55 out of 7) than videos of Barack Obama (3.19 out of 7).
“In Ng ' s [33] study, participants rated videos of Donald Trump as more fake (4.55 out of 7) than videos of Barack Obama (3.19 out of 7).”
From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [33]
The relationship between familiarity and detection accuracy is complicated by viewers' pre-existing perceptions of the depicted individuals.
“First, the relationship between familiarity and detection accuracy is complicated by viewers ' pre-existing perceptions of the depicted individuals [33].”
From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [33]
Cites
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · cited 1× · see citing works
“Apart from making the first two correct decisions, humans often systematically make the last two errors under uncertainty after (implicitly) considering the costs of the decisions (Tversky & Kahneman, 1974).”
Casler, K., Bickel, L., & Hackett, E. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.05.009 · Cited by 1,662 · see citing works
Vaccari, C., & Chadwick, A. (2020) · Social Media + Society · doi:10.1177/2056305120903408 · Cited by 978 · see citing works
Combating Deepfake Videos Using Blockchain and Smart Contracts
Hasan, H. R., & Salah, K. (2019) · IEEE Access · doi:10.1109/access.2019.2905689 · Cited by 339 · see citing works
Text-based Editing of Talking-head Video
Fried, O., Tewari, A., Zollhöfer, M., Finkelstein, A., Shechtman, E., Goldman, D. B., Genova, K., Jin, Z., Theobalt, C., & Agrawala, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323028 · Cited by 270 · see citing works
Do (microtargeted) deepfakes have real effects on political attitudes?
Dobber, T., Metoui, N., Trilling, D., Helberger, N., & de Vreese, C. (2021) · The International Journal of Press/Politics · doi:10.1177/1940161220944364 · Cited by 256 · see citing works
Deepfake detection by human crowds, machines, and machine-informed crowds
Groh, M., Epstein, Z., Firestone, C., & Picard, R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110013119 · Cited by 248 · see citing works
Fooled twice – people cannot detect deepfakes but think they can
Köbis, N., Doležalová, B., & Soraperra, I. (2021) · doi:10.2139/ssrn.3832978 · Cited by 194 · see citing works
Anticipating and Addressing the Ethical Implications of Deepfakes in the Context of Elections
Diakopoulos, N., & Johnson, D. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3474183 · Cited by 194 · see citing works
Fooled twice: People cannot detect deepfakes but think they can
Köbis, N. C., Doležalová, B., & Soraperra, I. (2021) · iScience · doi:10.1016/j.isci.2021.103364 · Cited by 193 · see citing works
Politics and porn: how news media characterizes problems presented by deepfakes
Gosse, C., & Burkell, J. (2020) · Critical Studies in Media Communication · doi:10.1080/15295036.2020.1832697 · Cited by 148 · see citing works
Effects of Disinformation Using Deepfake: The Protective Effect of Media Literacy Education
Hwang, Y., Ryu, J. Y., & Jeong, S.-H. (2021) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2020.0174 · Cited by 148 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Human Performance in Deepfake Detection: A Systematic Review
Somoray, K., Miller, D. J., & Holmes, M. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/1833228 · Cited by 19 · see citing works
Plohl, N., Mlakar, I., Aquilino, L., Bisconti, P., & Smrke, U. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2384821 · Cited by 9 · see citing works
- Authors
- Ng, Y.-L.
- Affiliations
- Hong Kong Baptist University (HK)
- Corresponding author
- Ng, Yu-Leung
- ORCID
- Ng, Yu-Leung
- Venue
- Current Psychology
- Published
- 2022-08-31
- Volume
- 42
- Issue
- 29
- Pages
- 25658-25669
- ISSN
- 1046-1310 (ISSN-L)
- 1936-4733
- OpenAlex
- W4293765150
- Cited by
- 19
- References
- 12
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 3 · 2023: 2
- FWCI
- 4.68
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-10-01)
- Open access
- closed
- Licence
- Locations
- Current Psychology (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Keywords
- deepfakes, error management theory, false positives, false negative errors, perceived dangerousness, perceived trustworthiness, misinformation detection, Barack Obama
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 ›
Are you an author, or do you see a problem with this page? Request a review
