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Psychological targeting as an effective approach to digital mass persuasion
Open accessAbstract
People are exposed to persuasive communication across many different contexts: Governments, companies, and political parties use persuasive appeals to encourage people to eat healthier, purchase a particular product, or vote for a specific candidate. Laboratory studies show that such persuasive appeals are more effective in influencing behavior when they are tailored to individuals' unique psychological characteristics. However, the investigation of large-scale psychological persuasion in the real world has been hindered by the questionnaire-based nature of psychological assessment. Recent research, however, shows that people's psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook Likes or Tweets. Capitalizing on this form of psychological assessment from digital footprints, we test the effects of psychological persuasion on people's actual behavior in an ecologically valid setting. In three field experiments that reached over 3.5 million individuals with psychologically tailored advertising, we find that matching the content of persuasive appeals to individuals' psychological characteristics significantly altered their behavior as measured by clicks and purchases. Persuasive appeals that were matched to people's extraversion or openness-to-experience level resulted in up to 40% more clicks and up to 50% more purchases than their mismatching or unpersonalized counterparts. Our findings suggest that the application of psychological targeting makes it possible to influence the behavior of large groups of people by tailoring persuasive appeals to the psychological needs of the target audiences. We discuss both the potential benefits of this method for helping individuals make better decisions and the potential pitfalls related to manipulation and privacy.
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In the citing paper
“Matz et al. (2017) reflect on their (Kosinski and Stillwell) decade-long research program on infering personality traits from social media users and its potential for mass manipulation, as evidenced in the adoption of similar techniques in the manipulation of elections.28”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Matz et al., 2017
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“Personality matching has been shown to increase the effectiveness of real-life persuasive communication59.”
From A psychometric framework for evaluating and shaping personality traits in large language models (2025) · cites this paper as [59]
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“companies report an increase in sales if salespersons demonstrate high extraversion and openness [38]”
From Interacting with a Chatbot-Based Advising System: Understanding the Effect of Chatbot Personality and User Gender on Behavior (2022) · cites this paper as [38]
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“and matching a message to an audience's personality improves its effectiveness [36 ].”
From Two social lives: how differences between online and offline interaction influence social outcomes (2020) · cites this paper as [36]
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Metadata
- Authors
- Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J.
- Affiliations
- Columbia University (US)
- Graduate School of Business, Stanford University, Stanford, CA 94305;
- University of Pennsylvania (US)
- University of Cambridge (GB)
- Corresponding author
- Matz, S. C.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-11-13
- Volume
- 114
- Issue
- 48
- Pages
- 12714-12719
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5715760
- OpenAlex
- W2768262152
- Cited by
- 892
- References
- 32
- Citations by year
- 2026: 86 · 2025: 127 · 2024: 113 · 2023: 105 · 2022: 125 · 2021: 110 · 2020: 115 · 2019: 72 · 2018: 39
- FWCI
- 48.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc-nd)
- Apollo
- Columbia Academic Commons (Columbia University)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Social Psychology › Psychology of Social Influence
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- personalized advertising, persuasive appeals, digital footprint, personality prediction, extraversion, openness to experience, behavioral influence, online purchase, click behavior, manipulation, privacy
- MeSH terms
- Direct-to-Consumer Advertising, Extraversion, Psychological, Humans, Individuality, Introversion, Psychological, Mass Behavior, Persuasive Communication, Psychometrics, Behavior Control, Social Media
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 ›
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