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PreprintCited by 1Misinformation and Its ImpactsMedia Influence and PoliticsSocial Media and Politics

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The Moderating Role of Perceived Trustworthiness in Explaining the Attitudinal Effects of Political Deepfakes

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Plohl, Nejc, Mlakar, Izidor, Aquilino, Letizia, Brienza, Michele, Bisconti, Piercosma, Smrke, Urška·2025·Computers in Human Behavior Reports
DOI 10.2139/ssrn.5351533
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Abstract

Research on political deepfakes has largely focused on detection accuracy, as well as broader psychological consequences such as uncertainty and mistrust. Less is known, however, about whether deepfake exposure influences issue-level political attitudes and under which conditions such effects occur. The present study investigated the attitudinal effects of political deepfakes on two polarizing issues, specifically climate change and immigration. In an online experiment conducted across Slovenia, Italy, and the United Kingdom ( N = 1124), participants were randomly exposed to one real video, a high-quality deepfake, or a low-quality deepfake promoting either positive or negative attitudes toward each topic. The study examined whether attitudes shifted after a single exposure and tested whether objective video characteristics, perceived trustworthiness, and political orientation moderated these effects. Deepfakes were about as persuasive as real videos, although overall attitudinal shifts were modest. Notably, perceived trustworthiness of the content emerged as a consistent moderator; deepfakes exerted stronger influence when their message was seen as credible. Perceived trustworthiness of the presentation (e.g., delivery, technical quality) also played a role, though more limited. Contrary to expectations, political orientation did not significantly moderate deepfake effects. The findings suggest that the persuasive power of deepfakes depends less on their technical sophistication and more on how credible their content appears to viewers, highlighting the need for interventions focused on fostering critical evaluation of political video content, particularly in the age of artificial intelligence-driven media manipulation.

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Research works printed but not linked: 46

  1. D Albarracin, 2008, Structure of attitudes: Judgments, memory, and implications for change, Attitudes and attitude change, 19
  2. Z Barua, 2020, Effects of misinformation on COVID-19 individual responses and recommendations for resilience of disastrous consequences of misinformation, Progress in Disaster Science, 8, 10.1016/j.pdisas.2020.100119 · DOI
  3. J Chandler, 2019, Online panels in social science research: Expanding sampling methods beyond Mechanical Turk, Behavior Research Methods, 51, 2022, 10.3758/s13428-019-01273-7 · DOI · Paper page
  4. A Diel, 2024, Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers, Computers in Human Behavior Reports, 16, 10.1016/j.chbr.2024.100538 · DOI · Paper page
  5. T Dobber, 2021, Do (microtargeted) deepfakes have real effects on political attitudes?, The International Journal of Press/Politics, 26, 69, 10.1177/1940161220944364 · DOI · Paper page
  6. C Doss, 2023, Deepfakes and scientific knowledge dissemination, Scientific Reports, 13, 10.1038/s41598-023-39944-3 · DOI · Paper page
  7. A H Eagly, 1993, The psychology of attitudes
  8. F Faul, 2007, G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences, Behavior Research Methods, 39, 175, 10.3758/BF03193146 · DOI · Paper page
  9. S Feldman, 2003, Values, ideology, and the structure of political attitudes, Oxford handbook of political psychology, 477
  10. M Friese, 2016, Implicit measures of attitudes and political voting behavior, Social and Personality Psychology Compass, 10, 188, 10.1111/spc3.12246 · DOI
  11. L R Glasman, 2006, Forming attitudes that predict future behavior: A metaanalysis of the attitude-behavior relation, Psychological Bulletin, 132, 778, 10.1037/0033-2909.132.5.778 · DOI · Paper page
  12. C M Greene, 2021, Quantifying the effects of fake news on behavior: Evidence from a study of COVID-19 misinformation, Journal of Experimental Psychology: Applied, 27, 773
  13. R L Greenspan, 2021, Pandemics and infodemics: Research on the effects of misinformation on memory, Human Behavior and Emerging Technologies, 3, 8, 10.1002/hbe2.228 · DOI
  14. A G Greenwald, 1998, Measuring individual differences in implicit cognition: The implicit association test, Journal of Personality and Social Psychology, 74, 1464, 10.1037/0022-3514.74.6.1464 · DOI · Paper page
  15. M Hameleers, 2022, You won't believe what they just said! The effects of political deepfakes embedded as vox populi on social media, Social Media and Society, 8
  16. M Hameleers, 2024, They would never say anything like this! Reasons to doubt political deepfakes, European Journal of Communication, 39, 56, 10.1177/02673231231184703 · DOI · Paper page
  17. A F Hayes, 2017, Introduction to mediation, moderation, and conditional process analysis: a regression-based approach
  18. S Hughes, 2021, Deepfaked online content is highly effective in manipulating attitudes and intentions
  19. Y Hwang, 2021, Effects of disinformation using deepfake: The protective effect of media literacy education, Cyberpsychology, Behavior, and Social Networking, 24, 188, 10.1089/cyber.2020.0174 · DOI · Paper page
  20. J T Jost, 2013, Hot" political cognition: Its self-, group-, and system serving purposes, Oxford handbook of social cognition, 851
  21. D M Kahan, 2015, The politically motivated reasoning paradigm, part 1: What politically motivated reasoning is and how to measure it, Emerging Trends in the Social and Behavioral Sciences, 10
  22. J Kahne, 2017, Educating for democracy in a partisan age: Confronting the challenges of motivated reasoning and misinformation, American Educational Research Journal, 54, 3, 10.3102/0002831216679817 · DOI
  23. Z Kunda, 1990, The case for motivated reasoning, Psychological Bulletin, 108, 480, 10.1037/0033-2909.108.3.480 · DOI · Paper page
  24. Y Lee, 2021, To believe or not to believe: framing analysis of content and audience response of top 10 deepfake videos on youtube, Cyberpsychology, Behavior, and Social Networking, 24, 153, 10.1089/cyber.2020.0176 · DOI
  25. J Lee, 2022, Something that they never said: Multimodal disinformation and source vividness in understanding the power of AI-enabled deepfake news, Media Psychology, 25, 531, 10.1080/15213269.2021.2007489 · DOI · Paper page
  26. S Lewandowsky, 2012, Misinformation and its correction: Continued influence and successful debiasing, Psychological Science in the Public Interest, 13, 106, 10.1177/1529100612451018 · DOI · Paper page
  27. M R Maniaci, 2014, Caring about carelessness: Participant inattention and its effects on research, Journal of Research in Personality, 48, 61, 10.1016/j.jrp.2013.09.008 · DOI · Paper page
  28. Merriam-Webster, Deepfake, Merriam-Webster.com dictionary
  29. Y L Ng, 2023, An error management approach to perceived fakeness of deepfakes: The moderating role of perceived deepfake targeted politicians' personality characteristics, Current Psychology, 42, 25658, 10.1007/s12144-022-03621-x · DOI · Paper page
  30. A Paivio, 1973, Picture superiority in free recall: Imagery or dual coding?, Cognitive Psychology, 5, 176, 10.1016/0010-0285(73)90032-7 · DOI
  31. A Panno, 2023, A measure of positive and negative perception of migration: Development and psychometric properties of the Positive and Negative Perception of Immigrants Scale (PANPIS), 23, 73
  32. N Plohl, 2024, Development and validation of the Perceived Deepfake Trustworthiness Questionnaire (PDTQ) in three languages, International Journal of Human-Computer Interaction, 1
  33. A Rijo, 2023, That's interesting! The role of epistemic emotions and perceived credibility in the relation between prior beliefs and susceptibility to fake-news, Computers in Human Behavior, 141, 10.1016/j.chb.2022.107619 · DOI
  34. N Schwarz, 2001, The construction of attitudes, Blackwell handbook of social psychology: Intraindividual processes, 436
  35. M Sedl�r, 2024, Connectedness to humanity is more important for attitudes toward immigrants than holistic thinking, cognitive flexibility, and attributional complexity, Personality and Individual Differences, 217
  36. G M Sinatra, 2012, Promoting attitude change and expressed willingness to take action toward climate change in college students, Proceedings of the 10th international conference on social media and society, 40, 136
  37. M W Susmann, 2023, How attitudes impact the continued influence effect of misinformation: The mediating role of discomfort, Personality and Social Psychology Bulletin, 49, 744, 10.1177/01461672221077519 · DOI
  38. S S�tterlin, 2023, Individual deep fake recognition skills are affected by viewer's political orientation, agreement with content and device used, International Conference on Human-Computer Interaction, 269
  39. T Talhelm, 2015, Liberals think more analytically (more "WEIRD") than conservatives, Personality and Social Psychology Bulletin, 41, 250, 10.1177/0146167214563672 · DOI
  40. K M D I Treen, 2020, Online misinformation about climate change, Wiley Interdisciplinary Reviews: Climate Change, 11
  41. M Westerlund, 2019, The emergence of deepfake technology: A review, Technology Innovation Management Review, 9, 40, 10.22215/timreview/1282 · DOI · Paper page
  42. L Whitmarsh, 2005, A study of public understanding of and response to climate change in the south of England
  43. L Whitmarsh, 2008, Are flood victims more concerned about climate change than other people? The role of direct experience in risk perception and behavioural response, Journal of Risk Research, 11, 351, 10.1080/13669870701552235 · DOI
  44. L Whitmarsh, 2011, Scepticism and uncertainty about climate change: Dimensions, determinants and change over time, Global Environmental Change, 21, 690, 10.1016/j.gloenvcha.2011.01.016 · DOI · Paper page
  45. C Wright, 2021, Media portrayals of immigration and refugees in hard and fake news and their impact on consumer attitudes, Howard Journal of Communications, 32, 331, 10.1080/10646175.2020.1810180 · DOI
  46. H K Zeng, 2023, Credibility of misinformation source moderates the effectiveness of corrective messages on social media, Public Understanding of Science

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Metadata

Authors
Plohl, N., Mlakar, I., Aquilino, L., Brienza, M., Bisconti, P., & Smrke, U.
Affiliations
  • University of Maribor (SI)
  • Università Cattolica del Sacro Cuore (IT)
  • Sapienza University of Rome (IT)
Corresponding author
Plohl, Nejc
ORCID
Venue
Computers in Human Behavior Reports
Publisher
Elsevier BV
Published
2025
Volume
23
Pages
101179-101179
ISSN
2451-9588 (ISSN-L)
DOI
10.2139/ssrn.5351533
OpenAlex
W4412401991
Cited by
1
References
0
Citations by year
2026: 1
FWCI
11.67
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Computers in Human Behavior Reports (publishedVersion, cc-by-nc-nd)
  • SSRN Electronic Journal (acceptedVersion)
  • dCOBISS.SI Digital Repository (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
HORIZON EUROPE Framework Programme (101094665)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Sociology and Political Science › Media Influence and Politics
  • Social Sciences › Communication › Social Media and Politics
Keywords
political deepfakes, perceived trustworthiness, political attitudes, attitude change, persuasion, climate change attitudes, immigration attitudes, political polarization, political orientation

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CoChat. (2026). Citation check: The Moderating Role of Perceived Trustworthiness in Explaining the Attitudinal Effects of Political Deepfakes. https://app.cochat.ai/papers/10.2139/ssrn.5351533
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Plohl, N., Mlakar, I., Aquilino, L., Brienza, M., Bisconti, P., & Smrke, U. (2025). The Moderating Role of Perceived Trustworthiness in Explaining the Attitudinal Effects of Political Deepfakes. Computers in Human Behavior Reports. https://doi.org/10.2139/ssrn.5351533