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 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.
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.
Integrity signals
Registry
Cites
Statistics
Figures


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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
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
Self-Reports in Organizational Research: Problems and Prospects
Podsakoff, P. M., & Organ, D. W. (1986) · Journal of Management · doi:10.1177/014920638601200408 · Cited by 17,102
An Introduction to the Five‐Factor Model and Its Applications
McCrae, R. R., & John, O. P. (1992) · Journal of Personality · doi:10.1111/j.1467-6494.1992.tb00970.x · Cited by 6,835
Interaction terms in logit and probit models
Ai, C., & Norton, E. C. (2003) · Economics Letters · doi:10.1016/s0165-1765(03)00032-6 · Cited by 4,599
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
The Role of Emotions in Marketing
Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999) · Journal of the Academy of Marketing Science · doi:10.1177/0092070399272005 · Cited by 3,349
The international personality item pool and the future of public-domain personality measures
Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. G. (2006) · Journal of Research in Personality · doi:10.1016/j.jrp.2005.08.007 · Cited by 3,185
Private traits and attributes are predictable from digital records of human behavior
Kosinski, M., Stillwell, D., & Graepel, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218772110 · Cited by 2,829
Self-citationWhat Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?
Levitt, S. D., & List, J. A. (2007) · Journal of Economic Perspectives · doi:10.1257/jep.21.2.153 · Cited by 2,668
Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions
Noar, S. M., Benac, C. N., & Harris, M. S. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.4.673 · Cited by 2,136
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
Self-citation
Cited by
Sorted by citations
Items 801–890 of 890
van Heusden, A. J. (2025) · Economic analysis of law in European legal scholarship · doi:10.1007/978-3-032-01468-9_2
Regulating Personalized Marketing: A Policy Perspective
van Heusden, A. J. (2025) · Economic analysis of law in European legal scholarship · doi:10.1007/978-3-032-01468-9_6
Online Consumer Manipulation: A Law and Economics Analysis of Personalized Advertising
van Heusden, A. J. (2025) · Economic analysis of law in European legal scholarship · doi:10.1007/978-3-032-01468-9_3
Choi, H., Lee, K., Northup, T., & Han, N. R. (2025) · International Marketing Review · doi:10.1108/imr-11-2023-0317
The impact of sensation seeking on consumer responses to branded content on short-video sharing apps
Dodoo, N. A., & Youn, S. (2025) · Journal of Marketing Communications · doi:10.1080/13527266.2025.2554627
Tamiosso, D., & Jaques, P. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04999-5_23
Feng, C., Zhang, S., Jiang, Z., & Dong, S. (2025) · Australasian Marketing Journal (AMJ) · doi:10.1177/14413582251374709
Emulating Reactive Workloads for Cyber-Human Systems: A Data-Driven Methodology
Muñoz, M. O., Klatzky, R. L., Satyanarayanan, M., & Gross, J. (2025) · IEEE Access · doi:10.1109/access.2025.3614639
Hyper-Personalised Marketing with Generative AI and Predictive Models
Pires, P. B., Santos, J. D., & Torres, A. I. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2502-6.ch004 · Cited by 1
Inhabiting the Face: From Fake to Deepfake
Ravetto‐Biagioli, K. (2025) · Theory Culture & Society · doi:10.1177/02632764251370095
The (In)Effectiveness of Psychological Targeting: A Meta‐Analytic Review
Perla, R., Maran, T. K., Bagci, B., Kraus, S., Kanbach, D. K., & Bouncken, R. B. (2025) · Psychology and Marketing · doi:10.1002/mar.70073 · Cited by 2
Nudging or nagging: The perils of persuasion
Guido, A., Tverskoi, D., Sergey Gavrilets, B., Sánchez, Á., & Andrighetto, G. (2025) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2025.107293
Ackermann, K. A., Furchheim, P., Bächler, J., Heim, N., & Lenggenhager, V. N. (2025) · Journal of Consumer Behaviour · doi:10.1002/cb.70051
Exploring communication strategies with esports fans: signaling physical location and popularity
Nolin, G., Karabas, I., & King, S. (2025) · Journal of Consumer Marketing · doi:10.1108/jcm-07-2024-7066 · Cited by 1
Jackson, K. M., Kiss, H., & Bergman, M. E. (2025) · British Food Journal · doi:10.1108/bfj-05-2025-0727 · Cited by 2
Leveraging complex network features improves vaccine stance classification
Durazzi, F., Barbieri, N., Faccin, M., Gore, D., & Remondini, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-27487-8
Aminpour, P., HELGESON, J. F., & Ferraro, P. J. (2023) · Figshare · doi:10.6084/m9.figshare.22609204
Kent, M. P., Bagnato, M., Remedios, L., Guimarães, J. S., Gillis, G., Soto, C., Hatoum, F., & Pritchard, M. (2024) · Figshare · doi:10.6084/m9.figshare.26741051
Yin, Y., Feng, X., Chen, Z., & Jia, J. S. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2025.12.005
Digital Transformation of Wine Opinion: From Critics to Peer Reviews Analysis
Dubois, M., Folwarczny, M., Cao, J., & Bender, T. (2026) · Springer eBooks · doi:10.1007/978-3-032-04851-6_9
Enhancing Debunking Effectiveness Through LLM-Based Personality Adaptation
Dell'Oglio, P., Bondielli, A., Marcelloni, F., & Passaro, L. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15632-7_23
Fagan, P., & Silveira, M. M. (2026) · Springer eBooks · doi:10.1007/978-3-032-12271-1_10
From Brain Maps to Behavioral Governance
Quintana, S. C. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4480-5.ch009
Algorithmic Segmentation of the Electorate: New Clustering Practices in Political Consulting
P'yanov, M. P. (2026) · Право и политика · doi:10.7256/2454-0706.2026.2.78365
Khan, M., & Ullah, H. (2026) · Innovation Economics · doi:10.11648/j.iecon.20260101.11
Nakashima, Y., Takeuchi, T., Kuzuoka, H., & Narumi, T. (2026) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.41-2_ids26-h
Dubosson, M., & Fragnière, E. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83549-534-620251005
Nguyen, B., Van, K. H., Pham, V. T., & Minh, C. P. T. (2026) · Innovative Marketing · doi:10.21511/im.22(1).2026.18
Algorithmic seduction: ethical boundaries in AI-powered consumer nudging
James, S., S, K. R. U. V., Thomas, B., & Jose, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1675412
Promoting sustainable clothing consumption through trait-based personalization in Instagram ads
Boerman, S. C., Kruikemeier, S., Remoortere, A. V., & Vliegenthart, R. (2026) · Applied Environmental Education & Communication · doi:10.1080/1533015x.2026.2628156
Pittman, M., Milfeld, T., & Haley, E. (2026) · Journal of Advertising Research · doi:10.1080/00218499.2025.2582341
Singh, V., Maheshwari, N., Khekare, G. S., & Rana, S. S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-5467-5.ch003
Kannen, C., & Montag, C. (2026) · Telematics and Informatics Reports · doi:10.1016/j.teler.2026.100324 · Cited by 1
Les différences culturelles dans l'usage de Facebook entre les Etats-Unis et la France
Brown, G. (2018) · theses.fr (ABES) · doi:10.70675/5a149c29zb947z4037z8e72z2b5b8f0ca18d
Application des normes sociales aux technologies persuasives : le cas de la rénovation énergétique
Corrégé, J.-B. (2018) · theses.fr (ABES) · doi:10.70675/c13bbb9az438az4077zb961zae30ada9defb
Three Essays on Consumer Social Cognition in a Technology-Rich World
Linares, C. (2022) · theses.fr (ABES) · doi:10.70675/4898d96az4b9bz443fz8877z1201e0ad2d7c
The Use of Artificial Intelligence for Personalized Advertising and Marketing.
Dhruv Jivrajani, S. M. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-32854
The Psychological Rewards of Online Self-Disclosure
Yu, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-5896-3.ch005
Norouzi, V., Ahmadian, D., Ballestra, L. V., & Almasizadeh, J. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133723
Tüketiyorum, Öyleyse Varım (4): Pazarlama - Gereklilik ya Manipulasyon? İnsan Doğası ve Psikolojisi
Yüksel, A. (2026) · Seyahat ve Otel İşletmeciliği Dergisi · doi:10.24010/soid.1670492
Divya, J. (2026) · REST Journal on Banking Accounting and Business · doi:10.46632/jbab/5/2/8
Montag, C. (2026) · Nervenheilkunde · doi:10.1055/a-2839-2620
Obschonka, M., Fisch, C. O., Beliaeva, T., Jabeur, S. B., Fernando, T., & Fookes, C. (2026) · Small Business Economics · doi:10.1007/s11187-026-01221-8
Governance of social media: a social ownership approach
Dorman, P. (2026) · International Review of Applied Economics · doi:10.1080/02692171.2026.2672999
Schilling, T., & Sergeeva, A. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2662520
Aguiar, T. R., Brooks, T. R., & Lopes, D. (2026) · Springer eBooks · doi:10.1007/978-3-031-52643-5_55-1
Selective Persuasion: Structural Stance Bias of Generative AI in Controversial Issues
黎, 樟., & 塔, 娜. (2026) · Global Journal of Media Studies · doi:10.26599/gjms.2026.9330009 · Cited by 1
Imomotebegha, T. (2026) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3774935.3803065
-, L. W. A., CHAALI, K., & HABRI, S. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.04.167
The Right to UnpredictabilityPreprint
Ochs, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6885690
Personalization in Marketing Communication: A Meta-Analysis
Eisend, M., Niewiadomska, D., & van Noort, G. (2026) · Journal of Marketing · doi:10.1177/00222429261460888
Grandi, B., Graziano, S., Cardinali, M. G., & Cristini, G. (2026) · British Food Journal · doi:10.1108/bfj-09-2025-1217
Government officials as social media influencers: Appeal and effects on destination eWOM
Sun, P., Ji, X., Jin, H., & Li, X. (2026) · Journal of Digital Economy · doi:10.1016/j.jdec.2026.05.003
Aalouche, O. E., & Messaoudi, F. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06061-7_30
Measuring consumer susceptibility to informative and normative online social influences
Stöckli, S., Shen, H., & Bartsch, F. (2026) · Journal of Business Research · doi:10.1016/j.jbusres.2026.116270
Kovalenko, Y., Nikitin, A., & Kuzina, Y. (2026) · American Impact Review · doi:10.66308/air.e2026017
Assessing the Influence of Personality on the Effectivity of Digital Nudges
Gottschewski-Meyer, P. O., Lang, F., der Landwehr, M. A., & Knackstedt, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29583-5_17
From Confidence to Spillover Effects: Additions to Support Theory in a Travel‐Related Setting
Bremermann-Reiser, S. M., Bläuer, L., Catarci, D., & Reijnen, E. (2026) · Applied Cognitive Psychology · doi:10.1002/acp.70240
Bowden-Green, T., Hinds, J., & Joinson, A. (2026) · European Journal of Marketing · doi:10.1108/ejm-10-2024-0866
Chen, C. S., & Chang, W.-H. (2026) · Geopolitics under Globalization · doi:10.21511/gg.07(1).2026.06
Scheffer, D., & Islam, M. Z. (2026) · BIBLIOTHEK Forschung und Praxis · doi:10.1515/bfp-2026-0017
Emotions Recognition Systems and Data Economy
Confortini, C. (2025) · Springer eBooks · doi:10.1007/978-3-031-93247-2_7
Narrative transportation in high-intensity services: The case of travelogues
Zhou, S., & Qu, Y. (2026) · Tourism Management Perspectives · doi:10.1016/j.tmp.2026.101509
Aguiar, T. R., Brooks, T. R., & Lopes, D. (2026) · Springer eBooks · doi:10.1007/978-3-031-52643-5_55-2
Opportunities and risks of big data for the methods and culture of psychological science
Ibasco, G. C., Götz, F. M., Clark, L., & Tracy, J. L. (2026) · Nature Reviews Psychology · doi:10.1038/s44159-026-00603-9 · Cited by 1
Gahn, C., Vrielink, J. G., & Kruikemeier, S. (2026) · Information Communication & Society · doi:10.1080/1369118x.2026.2703856
Implications of the SPCIS Framework for Information Warfare
Kashima, Y., Filinkov, A., & Wood, M. (2025) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-032-17478-9_15
Crisis governance, legitimacy, and consumer ethics in service systems
Koufodontis, N. I., & Gala, P. (2026) · Journal of Services Marketing · doi:10.1108/jsm-01-2026-0093
Targeting without tracking: personality-based ad-app matching from public discourse
Krijestorac, H., Garg, R., & Raghunathan, R. (2026) · SN Business & Economics · doi:10.1007/s43546-026-01310-6
Sowmya, D. S. (2025) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-8588-3.ch005
Ethics, Transparency, and Algorithmic Accountability in Broadcast Media
Özkan, A. V. C. I., & Gültepe, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3965-6.ch001
Prachitmutita, I., & Anutariya, C. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-36547-7_14
Tögel, J. (2026) · Journal of Communication Inquiry · doi:10.1177/01968599261486005
Dijital Empati ve Yapay Duygular
Tutal, B. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1418.c5720
Popping misinformation bubbles: persona-informed style-based messaging in Facebook ad experiments
Lee, J., Booth, E., Farid, H., & Rizoiu, M.-A. (2026) · Journal of Computational Social Science · doi:10.1007/s42001-026-00493-0
HIP to save: A typology of consumer saving with implications for theory and practice
Garbinsky, E. N., Cannon, C., & Mead, N. L. (2026) · Consumer Psychology Review · doi:10.1002/arcp.70012
Acevedo-Merlano, Á. (2026) · Journalism and Media · doi:10.3390/journalmedia7040197
Lindahl, O. K. V., Grym, J., Hakulinen, C. A., & Aspara, J. (2026) · Journal of Marketing Analytics · doi:10.1057/s41270-026-00546-3
James, R., Elangovan, N., Ganesh, L., & Dheepa, T. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9589-0.ch007
Hu, W., Sundie, J. M., & Guadagno, R. E. (2026) · De Gruyter eBooks · doi:10.1515/9783111574462-007
ЖУРАВЛЕВ, А. Л., & Kitova, D. A. (2026) · Институт психологии Российской академии наук Социальная и экономическая психология · doi:10.38098/ipran.sep_2026_43_3_01
Chen, X., Li, X., Yin, L., Zhu, H., Xiao, Z., & Peng, Y. J. (2026) · Systems · doi:10.3390/systems14101327
Velez, Y. R. (2026) · Political Psychology · doi:10.1111/pops.70206
Platform Power and the Child Consumer
Hariyani, D. Y., Nabiila, J. H., Badawi, M., Rustam, P., & Luande, N. A. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-4921-1.ch011
Ad Delivery Algorithms: The Hidden Arbiters of Political Messaging
Ali, M., Sapiezynski, P., Korolova, A., Mislove, A., & Rieke, A. (2019) · arXiv · doi:10.48550/arxiv.1912.04255 · Cited by 30
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 ›
Are you an author, or do you see a problem with this page? Request a review