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All the News That’s Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation

Author

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  • Kreps, Sarah
  • McCain, R. Miles
  • Brundage, Miles

Abstract

Online misinformation has become a constant; only the way actors create and distribute that information is changing. Advances in artificial intelligence (AI) such as GPT-2 mean that actors can now synthetically generate text in ways that mimic the style and substance of human-created news stories. We carried out three original experiments to study whether these AI-generated texts are credible and can influence opinions on foreign policy. The first evaluated human perceptions of AI-generated text relative to an original story. The second investigated the interaction between partisanship and AI-generated news. The third examined the distributions of perceived credibility across different AI model sizes. We find that individuals are largely incapable of distinguishing between AI- and human-generated text; partisanship affects the perceived credibility of the story; and exposure to the text does little to change individuals’ policy views. The findings have important implications in understanding AI in online misinformation campaigns.

Suggested Citation

  • Kreps, Sarah & McCain, R. Miles & Brundage, Miles, 2022. "All the News That’s Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation," Journal of Experimental Political Science, Cambridge University Press, vol. 9(1), pages 104-117, March.
    • Handle: RePEc:cup:jexpos:v:9:y:2022:i:1:p:104-117_8
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    Citations

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    Cited by:

    1. Ma, Rui & Wang, Xueqing & Yang, Guo-Rui, 2025. "Fighting fake news in the age of generative AI: Strategic insights from multi-stakeholder interactions," Technological Forecasting and Social Change, Elsevier, vol. 216(C).
    2. Rodriguez-Sanchez, Carla & Sancho-Esper, Franco Manuel & Casaló, Luis Vicente & López, Manuela, 2025. "Generative AI as a source of information on environmental Problems: Understanding its influence on Generation Z," Technology in Society, Elsevier, vol. 83(C).
    3. Ruth Potts & Alex Lord & John Sturzaker, 2024. "Automatic for the people? Problematising the potential of digital planning," Urban Studies, Urban Studies Journal Limited, vol. 61(12), pages 2435-2451, September.
    4. Sarin Sok & Kimkong Heng & Mengkorn Pum, 2025. "Investigating High School Students’ Attitudes Toward the Use of AI in Education: Evidence from Cambodia," SAGE Open, , vol. 15(3), pages 21582440251, July.
    5. Xiao, Kun & Zhang, Hongfeng, 2025. "Utility of decisions and beliefs based on bounded-confidence opinion dynamics with group conformity," Chaos, Solitons & Fractals, Elsevier, vol. 195(C).
    6. Alex Lyman & Bryce Hepner & Lisa P. Argyle & Ethan C. Busby & Joshua R. Gubler & David Wingate, 2025. "Balancing Large Language Model Alignment and Algorithmic Fidelity in Social Science Research," Sociological Methods & Research, , vol. 54(3), pages 1110-1155, August.
    7. Abel, Martin & Johnson, Reed, 2025. "AI Bias for Creative Writing: Subjective Assessment Versus Willingness to Pay," IZA Discussion Papers 17646, IZA Network @ LISER.
    8. Nygren, Thomas & Spearing, Emily & Fay, Nicolas & Vega, Davide & Hardwick, Isabella I. & Roozenbeek, Jon & Ecker, Ullrich K. H., 2025. "The Seven Roles of Artificial Intelligence: Potential and Pitfalls in Combating Misinformation," OSF Preprints j4gtv_v1, Center for Open Science.
    9. Alotaibi Jareh, 2025. "An In-depth Assessment of The Acceptability And Effectiveness of AI tools in Identifying Misinformation in Media: Literature Review," SAGE Open, , vol. 15(4), pages 21582440251, November.
    10. Thomas Davidson & Daniel Karell, 2025. "Integrating Generative Artificial Intelligence into Social Science Research: Measurement, Prompting, and Simulation," Sociological Methods & Research, , vol. 54(3), pages 775-793, August.
    11. Laghari, Amjad Hussain & Ping, Han & Memon, Shabana & Makhdoom, Zohaib Hussain, 2025. "Human vs. virtual influencers in Crisis: How attribution and crisis response shape brand trust," Journal of Retailing and Consumer Services, Elsevier, vol. 87(C).
    12. Codruț-Georgian Artene & Ciprian Oprișa & Cristian Nicolae Buțincu & Florin Leon, 2023. "Finding Patient Zero and Tracking Narrative Changes in the Context of Online Disinformation Using Semantic Similarity Analysis," Mathematics, MDPI, vol. 11(9), pages 1-26, April.
    13. Zachary Wojtowicz, 2024. "When and Why is Persuasion Hard? A Computational Complexity Result," Papers 2408.07923, arXiv.org, revised Sep 2025.
    14. Stefan Feuerriegel & Jochen Hartmann & Christian Janiesch & Patrick Zschech, 2024. "Generative AI," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(1), pages 111-126, February.

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