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Cited by 91Misinformation and Its ImpactsClimate Change Communication and PerceptionDigital Media Forensic Detection

Deepfakes and scientific knowledge dissemination

Open access
Doss, Christopher, Mondschein, Jared, Shu, Dule, Wolfson, Tal, Kopecky, Denise, Fitton-Kane, Valerie A., Bush, Lance, Tucker, Conrad·2023·Scientific Reports
DOI 10.1038/s41598-023-39944-3PMC PMC10439167

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Abstract

Science misinformation on topics ranging from climate change to vaccines have significant public policy repercussions. Artificial intelligence-based methods of altering videos and photos (deepfakes) lower the barriers to the mass creation and dissemination of realistic, manipulated digital content. The risk of exposure to deepfakes among education stakeholders has increased as learners and educators rely on videos to obtain and share information. We field the first study to understand the vulnerabilities of education stakeholders to science deepfakes and the characteristics that moderate vulnerability. We ground our study in climate change and survey individuals from five populations spanning students, educators, and the adult public. Our sample is nationally representative of three populations. We found that 27-50% of individuals cannot distinguish authentic videos from deepfakes. All populations exhibit vulnerability to deepfakes which increases with age and trust in information sources but has a mixed relationship with political orientation. Adults and educators exhibit greater vulnerability compared to students, indicating that those providing education are especially susceptible. Vulnerability increases with exposure to potential deepfakes, suggesting that deepfakes become more pernicious without interventions. Our results suggest that focusing on the social context in which deepfakes reside is one promising strategy for combatting deepfakes.

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  • Studies into the effects of ethnicity and age yield inconsistent results.

    “while studies into the e ff ects of ethnicity and age yield inconsistent results [32, 37, 43].”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [43]

    The Results section reports that race did not consistently moderate deepfake detection across the populations studied. It also reports an age-related decline in detection among adults and teachers that contrasts with earlier findings on other forms of digital misinformation. This supports variation in findings, but the paper does not establish that studies of ethnicity themselves yield inconsistent results. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Educators were more vulnerable to deepfakes than students.

    “In Doss et al. ' s [43] study, educators were more vulnerable to deepfakes than students.”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [43]

  • Older age is associated with poorer deepfake detection.

    “Doss et al. [43] observed that older age is associated with poorer detection.”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [43]

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1 of the 67 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

  1. [20]Mirsky Y Lee W The creation and detection of deepfakes: A survey ACM Comput. Surv. 2021 54 41
  2. [22]Ng AY Jordan MI On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes Adv. Neural Inf. Process. Syst. 2002 14 841 848
  3. [25]Siarohin, A., Lathuiliere, S., Tulyakov, S., Ricci, E. & Sebe, N. First order motion model for image animation. Advances in Neural Information Processing Systems 32 (NIPS 2019)32, 11 (2019).
  4. [32]Mittal, T., Bhattacharya, U., Chandra, R., Bera, A. & Manocha, D. Emotions don't lie. In Proceedings of the 28th ACM International Conference on Multimedia, pp 2823–2832 (2020).

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Authors
Doss, C., Mondschein, J., Shu, D., Wolfson, T., Kopecky, D., Fitton-Kane, V. A., Bush, L., & Tucker, C.
Affiliations
  • RAND Corporation (US)
  • Carnegie Mellon University (US)
Corresponding author
  • Doss, Christopher
  • Bush, Lance
  • Tucker, Conrad
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2023-08-18
Volume
13
Issue
1
Pages
13429-13429
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-023-39944-3
PMC
PMC10439167
OpenAlex
W4385980672
Cited by
90
References
67
Citations by year
2026: 34 · 2025: 31 · 2024: 22 · 2023: 3
FWCI
38.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Science Foundation (2039612, 2039613, and 2039614)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
Keywords
deepfakes, scientific misinformation, video manipulation, deepfake detection, science education, educators, students, climate change misinformation, trust in information sources, political orientation, age differences, science communication
MeSH terms
Information Sources, Adult, Artificial Intelligence, Educational Status, Humans, Students, Climate Change

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