Cited by 143Social Media and PoliticsMisinformation and Its ImpactsMedia Influence and Politics

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

Open access
Simchon, Almog, Edwards, Matthew, Lewandowsky, Stephan·2024·PNAS Nexus

Published version of a preprint

Abstract

The increasing availability of microtargeted advertising and the accessibility of generative artificial intelligence (AI) tools, such as ChatGPT, have raised concerns about the potential misuse of large language models 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 nonpersonalized 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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/pnasnexus/pgae035

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • While some people simply ignore online misinformation, this content is likely to penetrate specific groups, especially since AI may help automate the micro-targeting process in which thousands of persuasive messages can now be generated easily at scale.

    “While some people simply ignore online misinformation (31), this content is likely to penetrate specific groups, especially since AI may help automate the micro-targeting process in which thousands of persuasive messages can now be generated easily at scale (24).”
  • GPT can automate micro-targeting in a way that makes it more persuasive than standard nonpersonalized ads.

    “GPT can also automate micro-targeting in a way that makes it more persuasive than standard nonpersonalized ads (24).”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [1]Cadwalladr C. 2020 Jan. Fresh Cambridge analytica leak ‘shows global manipulation is out of control’. The Guardian.

Cited by

Sorted by citations

Authors
Simchon, A., Edwards, M., & Lewandowsky, S.
Affiliations
  • Ben-Gurion University of the Negev (IL)
  • University of Bristol (GB)
  • University of Potsdam (DE)
Corresponding author
Simchon, Almog
Venue
PNAS Nexus
Published
2024-01-29
Volume
3
Issue
2
Pages
pgae035-pgae035
ISSN
2752-6542 (ISSN-L)
OpenAlex
W4391322710
Cited by
143
References
17
Citations by year
2026: 50 · 2025: 59 · 2024: 30 · 2023: 3 · 2021: 1
FWCI
116.10
Type
journal article
Language
en
Open access
Locations
  • PNAS Nexus (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Bristol Research (University of Bristol) (publishedVersion, cc-by)
  • UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by)
  • Explore Bristol Research (submittedVersion, cc-by)
  • PsyArXiv (OSF Preprints) (submittedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
Funders
  • Humboldt Foundation in Germany
  • ERC (101020961)
Topics
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Sociology and Political Science › Media Influence and Politics
Keywords
political microtargeting, generative AI, large language models, ChatGPT, persuasion, personality inference, political communication, AI ethics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review