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Cited by 83Electoral Systems and Political ParticipationMedia Influence and PoliticsSocial Media and Politics

Quantifying the potential persuasive returns to political microtargeting

Open access
Tappin, Ben M., Wittenberg, Chloe, Hewitt, Luke B., Berinsky, Adam J., Rand, David G.·2023·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.2216261120PMC PMC10288628
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Abstract

Much concern has been raised about the power of political microtargeting to sway voters’ opinions, influence elections, and undermine democracy. Yet little research has directly estimated the persuasive advantage of microtargeting over alternative campaign strategies. Here, we do so using two studies focused on U.S. policy issue advertising. To implement a microtargeting strategy, we combined machine learning with message pretesting to determine which advertisements to show to which individuals to maximize persuasive impact. Using survey experiments, we then compared the performance of this microtargeting strategy against two other messaging strategies. Overall, we estimate that our microtargeting strategy outperformed these strategies by an average of 70% or more in a context where all of the messages aimed to influence the same policy attitude (Study 1). Notably, however, we found no evidence that targeting messages by more than one covariate yielded additional persuasive gains, and the performance advantage of microtargeting was primarily visible for one of the two policy issues under study. Moreover, when microtargeting was used instead to identify which policy attitudes to target with messaging (Study 2), its advantage was more limited. Taken together, these results suggest that the use of microtargeting—combining message pretesting with machine learning—can potentially increase campaigns’ persuasive influence and may not require the collection of vast amounts of personal data to uncover complex interactions between audience characteristics and political messaging. However, the extent to which this approach confers a persuasive advantage over alternative strategies likely depends heavily on context.

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  • The findings support dissenting voices on the persuasive impact of either customization or elaboration.

    “At the very least, they support dissenting voices on the persuasive impact of either customization or elaboration (11, 28, 74, 76, 77).”

    From Testing theories of political persuasion using AI (2025) · cites this paper as [11]

    The Discussion (page unavailable) questions sweeping claims about customization: microtargeting’s persuasive advantage was context dependent, and targeting by more personal characteristics did not amplify it. But the paper also reports sizable persuasive gains from microtargeting in one setting, and our copy does not address elaboration. It therefore supports a qualified dissenting view of customization, not the full claim. (LLM, conf=0.82)

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  • Evidence for successful targeting is mixed, and targeting one population runs the risk of backfiring on those who feel overlooked by the message.

    “Despite the evidence just discussed on moral reframing, evidence for successful targeting is mixed (cf.Druckman, 2024;Hernandez et al., 2023;Tappin et al., 2023); moreover, targeting one population runs the risk of backfiring on those who feel overlooked by the message (e.g., Hersh & Schaffner, 2013;Ostfeld, 2019).”

    From How to study democratic backsliding (2024) · cites this paper as Tappin et al., 2023

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Research works printed but not linked: 2

  1. [22]Open Rights Group, Public are kept in the dark over data driven political campaigning, poll finds. Open Rights Group (2020). https://www.openrightsgroup.org/press-releases/public-are-kept-in-the-dark-over-data-driven-political-campaigning-poll-finds/. Accessed 10 September 2022.
  2. [24]A. Coppock, Persuasion in Parallel (University of Chicago Press, Chicago, 2022).

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Authors
Tappin, B. M., Wittenberg, C., Hewitt, L. B., Berinsky, A. J., & Rand, D. G.
Affiliations
  • Royal Holloway University of London (GB)
  • Massachusetts Institute of Technology (US)
  • Stanford University (US)
Corresponding author
Tappin, Ben M.
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2023-06-12
Volume
120
Issue
25
Pages
e2216261120-e2216261120
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.2216261120
PMC
PMC10288628
OpenAlex
W4380302601
Cited by
83
References
44
Citations by year
2026: 22 · 2025: 30 · 2024: 24 · 2023: 6 · 2021: 1
FWCI
80.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • OSF Preprints (OSF Preprints) (submittedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
  • PsyArXiv (OSF Preprints) (submittedVersion)
Topics
  • Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
  • Social Sciences › Sociology and Political Science › Media Influence and Politics
  • Social Sciences › Communication › Social Media and Politics
Keywords
political microtargeting, persuasive influence, campaign messaging, machine learning, policy attitudes, personal data collection, democracy

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