The persuasive effects of political microtargeting in the age of generative artificial intelligence

Almog Simchon*, Matthew Edwards, Stephan Lewandowsky

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review


The increasing availability of microtargeted advertising and the accessibility of generative AI tools, such as ChatGPT, have raised concerns about the potential misuse of large language models (LLMs) in scaling microtargeting efforts for political purposes. Recent technological advancements, involving generative AI and personality inference from consumed text, can potentially create a highly scalable "manipulation machine'' that targets individuals based on their unique vulnerabilities without requiring human input. This paper presents four studies examining the effectiveness of this putative "manipulation machine''. The results demonstrate that personalized political ads tailored to individuals' personalities are more effective than non-personalized ads (Studies 1a and 1b). Additionally, we showcase the feasibility of automatically generating and validating these personalized ads on a large scale (Studies 2a and 2b). These findings highlight the potential risks of utilizing AI and microtargeting to craft political messages that resonate with individuals based on their personality traits. This should be an area of concern to ethicists and policy makers.
Original languageEnglish
Article numberpgae035
JournalPNAS Nexus
Issue number2
Early online date29 Jan 2024
Publication statusPublished - 1 Feb 2024

Bibliographical note

This research was supported by a large grant from the Volkswagen Foundation (“Reclaiming individual autonomy and democratic discourse online”). S.L. was also supported by funding from the Humboldt Foundation in Germany and by the European Research Council (ERC) Advanced Grant (101020961, PRODEMINFO).

Structured keywords

  • Cyber Security
  • TeDCog


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