Evidence map›Paper›PMID 39875647›Full record

ArticleCommunications psychology2025

Warning people that they are being microtargeted fails to eliminate persuasive advantage.

Fabio Carrella, Almog Simchon, Matthew Edwards, Stephan Lewandowsky

Abstract read
In one paragraph

Article in Communications psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fabio CarrellaSchool of Psychological Science, University of Bristol, Bristol, UK. fabio.carrella@bristol.ac.uk.ORCID http://orcid.org/0000-0003-4918-3875
Almog SimchonDepartment of Psychology, Ben-Gurion University of the Negev, Beer Sheva, Israel.ORCID http://orcid.org/0000-0003-2629-2913
Matthew EdwardsSchool of Computer Science, University of Bristol, Bristol, UK.
Stephan LewandowskySchool of Psychological Science, University of Bristol, Bristol, UK.ORCID http://orcid.org/0000-0003-1655-2013

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 101020961EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 101094752
6 · The paper itself

Abstract

The practice of microtargeting in politics, involving tailoring persuasive messages to individuals based on personal vulnerabilities, has raised manipulation concerns. As microtargeting's persuasive benefits are well-established and its use facilitated by AI tools and personality-inference models, ethical and regulatory concerns are magnified. Here, we explore countering microtargeting effects by creating a warning signal deployed when users encounter personality-tailored political ads. Three studies evaluated the effectiveness of warning "popups" against potential microtargeting by comparing persuasiveness of targeted vs. non-targeted messages with and without popups. Using within subject-designs, Studies 1 (N = 666), 2a (N = 432), and 2b (N = 669) reveal a targeting effect, with targeted ads deemed more persuasive than non-targeted ones. More important, the presence of a warning popup had no meaningful impact on persuasiveness. Overall, across the three studies, personality-targeted ads were significantly more persuasive than non-targeted ones, and this advantage persisted despite warnings. Given the focus on transparency in initiatives like the EU's AI Act, our finding that warnings have little effect has potential policy implications.

Identifiers

PMID39875647
PMCPMC11774753

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.