CoChat
Sign Up
Cited by 892Decision-Making and Behavioral EconomicsPsychology of Social InfluenceDigital Marketing and Social Media

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Psychological targeting as an effective approach to digital mass persuasion

Open access
Matz, S. C., Kosinski, M., Nave, G., Stillwell, D. J.·2017·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1710966114PMC PMC5715760
Open this paper in CoChat — free account

Abstract

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.

Overview

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 · 10.1073/pnas.1710966114

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1. Examples of ads aimed at audiences characterized by high and low extraversion (A) as well as high and low openness (B). Fig. 1A, Left courtesy of Caiaimage/Paul Bradbury/OJO+/Getty Images; Fig. 1A, Right courtesy of Hybrid Images/Cultura/Getty Images.
Fig. 1. Examples of ads aimed at audiences characterized by high and low extraversion (A) as well as high and low openness (B). Fig. 1A, Left courtesy of Caiaimage/Paul Bradbury/OJO+/Getty Images; Fig. 1A, Right courtesy of Hybrid Images/Cultura/Getty Images.CC BY-NC-ND · PubMed Central
Fig. 2. Interaction effects of audience and ad personality on conversion rates in study 1 (Left) and study 2 (Right).
Fig. 2. Interaction effects of audience and ad personality on conversion rates in study 1 (Left) and study 2 (Right).CC BY-NC-ND · PubMed Central

Verification

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?

Self-check

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

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

Citations

Cites

Show all 32 references

Cited by

Sorted by citations

Items 301–400 of 890

Related

See all

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.
ORCID
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
DOI
10.1073/pnas.1710966114
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
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 ›

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