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Towards Generalizable AI-Assisted Misinformation Inoculation: Protecting Confidence Against False Election Narratives

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Listed:
  • Mitchell Linegar
  • Betsy Sinclair
  • Sander van der Linden
  • R. Michael Alvarez

Abstract

We present a generalizable AI-assisted framework for rapidly generating effective "prebunking" interventions against misinformation. Like mRNA vaccine platforms, our approach uses a stable template structure that can be quickly adapted to counter emerging false narratives. In a preregistered two-wave experiment with 4,293 U.S. registered voters, we test this framework against politically-charged election misinformation -- one of the most challenging domains for misinformation intervention. Our design directly tests scalability by comparing human-reviewed and purely AI-generated inoculation messages. We find that LLM-generated prebunking significantly reduced belief in election rumors (persisting for at least one week) and increased confidence in election integrity across partisan lines. Purely AI-generated messages proved as effective as human-reviewed versions, with some achieving larger protective effects, demonstrating that effective misinformation inoculation can be achieved at machine speed without proportional human effort, offering a scalable defense against the accelerating threat of false narratives across all domains.

Suggested Citation

  • Mitchell Linegar & Betsy Sinclair & Sander van der Linden & R. Michael Alvarez, 2024. "Towards Generalizable AI-Assisted Misinformation Inoculation: Protecting Confidence Against False Election Narratives," Papers 2410.19202, arXiv.org, revised Jun 2025.
    • Handle: RePEc:arx:papers:2410.19202
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    References listed on IDEAS

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    1. Cecilie S. Traberg & Jon Roozenbeek & Sander van der Linden, 2022. "Psychological Inoculation against Misinformation: Current Evidence and Future Directions," The ANNALS of the American Academy of Political and Social Science, , vol. 700(1), pages 136-151, March.
    2. R. Michael Alvarez & Jian Cao & Yimeng Li, 2021. "Voting Experiences, Perceptions of Fraud, and Voter Confidence," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 1225-1238, July.
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