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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Matz et al. (2017) reflect on their decade-long research program on inferring 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.
“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
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredPersonality matching has been shown to increase the effectiveness of real-life persuasive communication.
“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]
Companies report an increase in sales if salespersons demonstrate high extraversion and openness.
“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]
Matching a message to an audience's personality improves its effectiveness.
“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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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 · see citing works
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 · see citing works
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 5,466 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationComputer-based personality judgments are more accurate than those made by humans
Youyou, W., Kosinski, M., & Stillwell, D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418680112 · Cited by 1,086 · see citing works
Self-citationRegulatory Fit and Persuasion: Transfer From "Feeling Right."
Cesario, J., Grant, H., & Higgins, E. T. (2004) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.86.3.388 · Cited by 1,014 · see citing works
Kosiński, M., Matz, S., Gosling, S. D., Popov, V., & Stillwell, D. J. (2015) · American Psychologist · doi:10.1037/a0039210 · Cited by 926 · see citing works
Self-citationEnhancement and denial in socially desirable responding.
Paulhus, D. L., & Reid, D. B. (1991) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.60.2.307 · Cited by 824 · see citing works
Automatic personality assessment through social media language.
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L. H., & Seligman, M. E. P. (2015) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000020 · Cited by 802 · see citing works
Self-citationPersonality in 100,000 Words: A large-scale analysis of personality and word use among bloggers
Yarkoni, T. (2010) · Journal of Research in Personality · doi:10.1016/j.jrp.2010.04.001 · Cited by 637 · see citing works
Predicting Personality from Twitter
Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011) · International Conference on Social Computing · doi:10.1109/passat/socialcom.2011.33 · Cited by 613 · see citing works
FEELTRACE: an instrument for recording perceived emotion in real time
Cowie, R., Douglas‐Cowie, E., Savvidou, S. M., & McMahon, E. R. (2000) · Research Portal (Queen's University Belfast) · Cited by 539 · see citing works
Hirsh, J. B., Kang, S. K., & Bodenhausen, G. V. (2012) · Psychological Science · doi:10.1177/0956797611436349 · Cited by 506 · see citing works
MoodScope: building a mood sensor from smartphone usage patterns
LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013) · Proceeding of the 11th annual international conference on Mobile systems, applications, and services · doi:10.1145/2462456.2464449 · Cited by 444 · see citing works
Emotion-Induced Engagement in Internet Video Advertisements
Teixeira, T. S., Wedel, M., & Pieters, R. G. M. (2011) · Journal of Marketing Research · doi:10.1509/jmr.10.0207 · Cited by 439 · see citing works
Regulatory Fit and Persuasion: Basic Principles and Remaining Questions
Cesario, J., Higgins, E. T., & Scholer, A. A. (2007) · Social and Personality Psychology Compass · doi:10.1111/j.1751-9004.2007.00055.x · Cited by 371 · see citing works
Personality in cyberspace: Personal web sites as media for personality expressions and impressions.
Marcus, B., Machilek, F., & Schütz, A. (2006) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.90.6.1014 · Cited by 302 · see citing works
Wheeler, S. C., Petty, R. E., & Bizer, G. Y. (2005) · Journal of Consumer Research · doi:10.1086/426613 · Cited by 291 · see citing works
Hauser, J. R., Urban, G. L., Liberali, G., & Braun, M. I. (2009) · Marketing Science · doi:10.1287/mksc.1080.0459 · Cited by 291 · see citing works
Mann, T. L., Sherman, D. K., & Updegraff, J. A. (2004) · Health Psychology · doi:10.1037/0278-6133.23.3.330 · Cited by 242 · see citing works
Pathological gambling and the five-factor model of personality
Bagby, R. M., Vachon, D. D., Bulmash, E. L., Toneatto, T., Quilty, L. C., & Costa, P. T. (2007) · Personality and Individual Differences · doi:10.1016/j.paid.2007.02.011 · Cited by 187 · see citing works
What Do Laboratory Experiments Tell Us About the Real World
Levitt, S. D., & List, J. A. (2006) · Cited by 176 · see citing works
Moon, Y. (2002) · Journal of Consumer Psychology · doi:10.1016/s1057-7408(16)30083-3 · Cited by 141 · see citing works
Segalin, C., Perina, A., Cristani, M., & Vinciarelli, A. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2516994 · Cited by 135 · see citing works
Dynamics of Communicator and Audience Power: The Persuasiveness of Competence versus Warmth
Dubois, D., Rucker, D. D., & Galinsky, A. D. (2016) · Journal of Consumer Research · doi:10.1093/jcr/ucw006 · Cited by 131 · see citing works
Moon, Y. (2002) · Journal of Consumer Psychology · doi:10.1207/15327660260382351 · Cited by 47 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · see citing works
“Furthermore, countless companies strive for the opposite of human autonomy, employing more and more subtle techniques for manipulating user behavior via micro targeting, nudging, UX-design and so on (Fogg 2003; Matz et al. 2017).”
MethodTask-Dependent Algorithm Aversion
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019) · Springer eBooks · doi:10.1007/978-3-031-88067-4_21 · Cited by 1,596 · see citing works
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00834-7 · Cited by 1,409 · see citing works
“…that generative AI can automate the creation of personalized marketing content, for instance, different sales slogans for introverts vs. extroverts (Matz et al. 2017) or other personality traits as personalized marketing content is more effective than a one-content-fits-all approach (Matz et al.…”
The psychological drivers of misinformation belief and its resistance to correction
Ecker, U. K. H., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., Kendeou, P., Vraga, E. K., & Amazeen, M. A. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-021-00006-y · Cited by 1,372 · see citing works
Consumers and artificial intelligence: An experiential perspective
Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021) · Journal of Marketing · doi:10.1177/0022242920953847 · Cited by 1,193 · see citing works
Countering Misinformation and Fake News Through Inoculation and Prebunking
Lewandowsky, S., & van der Linden, S. (2021) · European Review of Social Psychology · doi:10.1080/10463283.2021.1876983 · Cited by 773 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
Misinformation: susceptibility, spread, and interventions to immunize the public
van der Linden, S. (2022) · Nature Medicine · doi:10.1038/s41591-022-01713-6 · Cited by 570 · see citing works
On the psychology of TikTok use: a first glimpse from empirical findings
Montag, C., Yang, H., & Elhai, J. D. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.641673 · Cited by 559 · see citing works
Small Effects: The Indispensable Foundation for a Cumulative Psychological Science
Götz, F. M., Gosling, S. D., & Rentfrow, P. J. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620984483 · Cited by 553 · see citing works
User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection
Isaak, J., & Hanna, M. J. (2018) · Computer · doi:10.1109/mc.2018.3191268 · Cited by 700 · see citing works
Technology, autonomy, and manipulation
Susser, D., Rössler, B., & Nissenbaum, H. F. (2019) · Internet Policy Review · doi:10.14763/2019.2.1410 · Cited by 518 · see citing works
Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools
Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2020) · Psychological Science in the Public Interest · doi:10.1177/1529100620946707 · Cited by 479 · see citing works
Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective
Hermann, E. (2021) · Journal of Business Ethics · doi:10.1007/s10551-021-04843-y · Cited by 458 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · PhilPapers (PhilPapers Foundation) · Cited by 394 · see citing works
Montag, C., Lachmann, B., Herrlich, M., & Zweig, K. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16142612 · Cited by 387 · see citing works
“[46] demonstrating how clicks and buys can be boosted by the use of personalized ads.”
BackgroundThe Ethics of Digital Well-Being: A Thematic Review
Burr, C., Taddeo, M., & Floridi, L. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00175-8 · Cited by 374 · see citing works
Making conversations with chatbots more personalized
Shumanov, M., & Johnson, L. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106627 · Cited by 363 · see citing works
Predicting personality from patterns of behavior collected with smartphones
Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., Völkel, S. T., Schuwerk, T., Oldemeier, M., Ullmann, T., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1920484117 · Cited by 362 · see citing works
The Potential of Generative AI for Personalized Persuasion at Scale
Matz, S., Teeny, J., Vaid, S. S., Peters, H., Harari, G. M., & Cerf, M. (2023) · doi:10.31234/osf.io/rn97c · Cited by 312 · see citing works
Self-citationKopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2021) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2021.11.002 · Cited by 323 · see citing works
On the conversational persuasiveness of GPT-4
Salvi, F., Ribeiro, M. H., Gallotti, R., & West, R. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02194-6 · Cited by 326 · see citing works
The potential of generative AI for personalized persuasion at scale
Matz, S. C., Teeny, J. D., Vaid, S. S., Peters, H., Harari, G. M., & Cerf, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-53755-0 · Cited by 315 · see citing works
“Compared to non-personalized communication, matching the content of a persuasive message (e.g., its language or visuals) to the psychological profile of its recipient enhances its effectiveness (e.g., 4,5 ).”
BackgroundSelf-citationHinds, J. M., Williams, E., & Joinson, A. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102498 · Cited by 281 · see citing works
Shepherd, D. A., & Majchrzak, A. (2022) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2022.106227 · Cited by 301 · see citing works
Two social lives: how differences between online and offline interaction influence social outcomes
Lieberman, A., & Schroeder, J. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2019.06.022 · Cited by 252 · see citing works
A Review and Conceptual Framework for Understanding Personalized Matching Effects in Persuasion
Teeny, J. D., Siev, J. J., Briñol, P., & Petty, R. E. (2020) · Journal of Consumer Psychology · doi:10.1002/jcpy.1198 · Cited by 259 · see citing works
Artificial intelligence and consumer behavior: From predictive to generative AI
Hermann, E., & Puntoni, S. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114720 · Cited by 284 · see citing works
Neuroticism and emotional risk during the COVID-19 pandemic
Kroencke, L., Geukes, K., Utesch, T., Kuper, N., & Back, M. D. (2020) · Journal of Research in Personality · doi:10.1016/j.jrp.2020.104038 · Cited by 231 · see citing works
How behavioural sciences can promote truth, autonomy and democratic discourse online
Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., & Hertwig, R. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0889-7 · Cited by 223 · see citing works
Zarouali, B., Dobber, T., Pauw, G. D., & de Vreese, C. H. (2020) · Communication Research · doi:10.1177/0093650220961965 · Cited by 225 · see citing works
Blagov, P. S. (2020) · Social Psychological and Personality Science · doi:10.1177/1948550620936439 · Cited by 219 · see citing works
Mõttus, R., Wood, D., Condon, D. M., Back, M. D., Baumert, A., Costantini, G., Epskamp, S., Greiff, S., Johnson, W., Lukaszewski, A. W., et al. (2020) · European Journal of Personality · doi:10.1002/per.2311 · Cited by 229 · see citing works
Hermann, E. (2022) · New Media & Society · doi:10.1177/14614448211022702 · Cited by 214 · see citing works
Auditing Partisan Audience Bias within Google Search
Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274417 · Cited by 209 · see citing works
Musical Preferences Predict Personality: Evidence From Active Listening and Facebook Likes
Nave, G., Minxha, J., Greenberg, D. M., Kosiński, M., Stillwell, D. J., & Rentfrow, P. J. (2018) · Psychological Science · doi:10.1177/0956797618761659 · Cited by 209 · see citing works
Self-citationFacial recognition technology can expose political orientation from naturalistic facial images
Kosiński, M. (2021) · Scientific Reports · doi:10.1038/s41598-020-79310-1 · Cited by 205 · see citing works
The power of generative marketing: Can generative AI create superhuman visual marketing content?
Hartmann, J., Exner, Y., & Domdey, S. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2024.09.002 · Cited by 229 · see citing works
The Digital Transformation of News Media and the Rise of Disinformation and Fake NewsPreprint
Martens, B., Aguiar, L. L. M., GGmez, E., & Mueller‐Langer, F. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3164170 · Cited by 210 · see citing works
An Analysis of the Interaction Between Intelligent Software Agents and Human Users
Burr, C., Cristianini, N., & Ladyman, J. (2018) · Minds and Machines · doi:10.1007/s11023-018-9479-0 · Cited by 184 · see citing works
Public Health Messaging during the COVID-19 Pandemic and Beyond: Lessons from Communication Science
Nan, X., Iles, I. A., Yang, B. K., & Ma, Z. (2021) · Health Communication · doi:10.1080/10410236.2021.1994910 · Cited by 183 · see citing works
A manifesto for applying behavioural science
Hallsworth, M. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01555-3 · Cited by 191 · see citing works
Information gerrymandering and undemocratic decisions
Stewart, A. J., Mosleh, M., Diakonova, M., Arechar, A. A., Rand, D. G., & Plotkin, J. B. (2019) · Nature · doi:10.1038/s41586-019-1507-6 · Cited by 176 · see citing works
Smart nudging: How cognitive technologies enable choice architectures for value co-creation
Mele, C., Spena, T. R., Kaartemo, V., & Marzullo, M. L. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.004 · Cited by 182 · see citing works
Mosleh, M., Pennycook, G., & Rand, D. G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228882 · Cited by 172 · see citing works
The effects of trait-based personalization in social media advertising
Winter, S., Masłowska, E., & Vos, A. L. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106525 · Cited by 171 · see citing works
Personality Research and Assessment in the Era of Machine Learning
Stachl, C., Pargent, F., Hilbert, S., Harari, G. M., Schoedel, R., Vaid, S. S., Gosling, S. D., & Bühner, M. (2020) · European Journal of Personality · doi:10.1002/per.2257 · Cited by 165 · see citing works
Kozyreva, A., Lorenz-Spreen, P., Hertwig, R., Lewandowsky, S., & Herzog, S. M. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-021-00787-w · Cited by 171 · see citing works
Buchanan, T., & Benson, V. (2019) · Social Media + Society · doi:10.1177/2056305119888654 · Cited by 151 · see citing works
Ethical machines: The human-centric use of artificial intelligence
Lepri, B., Oliver, N., & Pentland, A. (2021) · iScience · doi:10.1016/j.isci.2021.102249 · Cited by 149 · see citing works
Nudge Me Right: Personalizing Online Security Nudges to People's Decision-Making Styles
Péer, E., Egelman, S., Harbach, M., Malkin, N., Mathur, A., & Frik, A. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106347 · Cited by 148 · see citing works
Three Painful Truths About Social Media
Deibert, R. J. (2019) · Journal of Democracy · doi:10.1353/jod.2019.0002 · Cited by 145 · see citing works
Bottom-Up and Top-Down: Predicting Personality with Psycholinguistic and Language Model Features
Mehta, Y., Fatehi, S., Kazameini, A., Stachl, C., Cambria, E., & Eetemadi, S. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00146 · Cited by 141 · see citing works
The persuasive effects of political microtargeting in the age of generative artificial intelligence
Simchon, A., Edwards, M., & Lewandowsky, S. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae035 · Cited by 143 · see citing works
Gambling Marketing from 2014 to 2018: a Literature Review
Newall, P. W. S., Moodie, C. S., Reith, G., Stead, M., Critchlow, N., Morgan, A., & Dobbie, F. (2019) · Current Addiction Reports · doi:10.1007/s40429-019-00239-1 · Cited by 139 · see citing works
Making America Hate Again? Twitter and Hate Crime Under TrumpPreprint
MMller, K., & Schwarz, C. R. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3149103 · Cited by 128 · see citing works
Human and Computer Personality Prediction From Digital Footprints
Hinds, J. M., & Joinson, A. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721419827849 · Cited by 135 · see citing works
Montag, C., & Diefenbach, S. (2018) · Sustainability · doi:10.3390/su10020415 · Cited by 153 · see citing works
Reddit: A Gold Mine for Personality Prediction
Gjurković, M., & Šnajder, J. (2018) · doi:10.18653/v1/w18-1112 · Cited by 127 · see citing works
Privacy in the age of psychological targeting
Matz, S., Appel, R. E., & Kosiński, M. (2019) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2019.08.010 · Cited by 127 · see citing works
Self-citationReynolds, J., Stautz, K., Pilling, M., van der Linden, S., & Marteau, T. (2020) · Royal Society Open Science · doi:10.1098/rsos.190522 · Cited by 109 · see citing works
A Framework for the Study of Persuasion
Druckman, J. N. (2022) · Annual Review of Political Science · doi:10.1146/annurev-polisci-051120-110428 · Cited by 126 · see citing works
Bayer, E., Srinivasan, S., Riedl, E. J., & Skiera, B. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.02.002 · Cited by 120 · see citing works
Cambridge Analytica’s black box
Hu, M. (2020) · Big Data & Society · doi:10.1177/2053951720938091 · Cited by 118 · see citing works
Evaluating the persuasive influence of political microtargeting with large language models
Hackenburg, K., & Margetts, H. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2403116121 · Cited by 114 · see citing works
“This finding builds on prior work suggesting that political advertisements have increased persuasive power when tailored to an individual's position on the introversion–extroversion spectrum (29) or other psychological characteristics (30).”
BackgroundGötz, F. M., Gvirtz, A., Galinsky, A. D., & Jachimowicz, J. (2020) · American Psychologist · doi:10.1037/amp0000740 · Cited by 114 · see citing works
Itani, O. S., Haddad, R. E., & Kalra, A. (2019) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.101980 · Cited by 112 · see citing works
Deploying artificial intelligence in services to AID vulnerable consumers
Hermann, E., Williams, G. Y., & Puntoni, S. (2023) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-023-00986-8 · Cited by 113 · see citing works
Argyris, Y. A., Muqaddam, A., & Miller, S. R. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102347 · Cited by 109 · see citing works
How is extraversion related to social media use? A literature review
Bowden-Green, T., Hinds, J. M., & Joinson, A. (2020) · Personality and Individual Differences · doi:10.1016/j.paid.2020.110040 · Cited by 101 · see citing works
Cognitive Reflection and the 2016 U.S. Presidential Election
Pennycook, G., & Rand, D. G. (2018) · Personality and Social Psychology Bulletin · doi:10.1177/0146167218783192 · Cited by 99 · see citing works
Running field experiments using Facebook split test
Orazi, D. C., & Johnston, A. C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.06.053 · Cited by 95 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Understanding Detrimental Aspects of Social Media Use: Will the Real Culprits Please Stand Up?
Montag, C., & Hegelich, S. (2020) · Frontiers in Sociology · doi:10.3389/fsoc.2020.599270 · Cited by 92 · see citing works
Beiser-McGrath, L. F., & Huber, R. A. (2018) · Climatic Change · doi:10.1007/s10584-018-2260-9 · Cited by 91 · see citing works
Matz, S., & Harari, G. M. (2020) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000297 · Cited by 90 · see citing works
Self-citationAsimakopoulos, G., Antonopoulou, H., Giotopoulos, K. C., & Halkiopoulos, C. (2025) · Societies · doi:10.3390/soc15020040 · Cited by 90 · see citing works
Large language models can infer psychological dispositions of social media users
Peters, H., & Matz, S. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae231 · Cited by 102 · see citing works
Self-citationBayer, J., Bitiukova, N., Bárd, P., Szakács, J., Alemanno, A., & Uszkiewicz, E. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3409279 · Cited by 82 · see citing works
Quantifying the potential persuasive returns to political microtargeting
Tappin, B. M., Wittenberg, C., Hewitt, L. B., Berinsky, A. J., & Rand, D. G. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2216261120 · Cited by 83 · see citing works
Human or Robot? Consumer Responses to Radical Cognitive Enhancement Products
Castelo, N., Schmitt, B. H., & Sárváry, M. (2019) · Journal of the Association for Consumer Research · doi:10.1086/703462 · Cited by 81 · see citing works
Joyal‐Desmarais, K., Scharmer, A., Madzelan, M. K., See, J. V., Rothman, A. J., & Snyder, M. (2022) · Psychological Bulletin · doi:10.1037/bul0000377 · Cited by 88 · see citing works
Boerman, S. C., Kruikemeier, S., & Bol, N. (2021) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2021.100144 · Cited by 80 · see citing works
Shen, M., Lu, Y., Wei, K. H., & Cui, Q. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109839 · Cited by 80 · see citing works
Ramon, Y., Martens, D., Provost, F., & Evgeniou, T. (2020) · Advances in Data Analysis and Classification · doi:10.1007/s11634-020-00418-3 · Cited by 79 · see citing works
Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention
Lynn, V., Balasubramanian, N., & Schwartz, H. A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.472 · Cited by 75 · see citing works
Neave, L., Tzemou, E., & Fastoso, F. (2020) · Psychology and Marketing · doi:10.1002/mar.21308 · Cited by 76 · see citing works
Burr, C., & Cristianini, N. (2019) · Minds and Machines · doi:10.1007/s11023-019-09497-4 · Cited by 75 · see citing works
Using AI predicted personality to enhance advertising effectiveness
Shumanov, M., Cooper, H. B., & Ewing, M. T. (2021) · European Journal of Marketing · doi:10.1108/ejm-12-2019-0941 · Cited by 79 · see citing works
Alkhwaldi, A. F. (2024) · Sustainability · doi:10.3390/su16020934 · Cited by 83 · see citing works
Barceló, J., & Sheen, G. C.-H. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242764 · Cited by 74 · see citing works
Political Manipulation and Internet Advertising Infrastructure
Crain, M., & Nadler, A. (2019) · Journal of Information Policy · doi:10.5325/jinfopoli.9.1.0370 · Cited by 73 · see citing works
Newall, P. W. S. (2018) · Addiction Research & Theory · doi:10.1080/16066359.2018.1474206 · Cited by 73 · see citing works
Field studies of psychologically targeted ads face threats to internal validity
Eckles, D., Gordon, B. R., & Johnson, G. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1805363115 · Cited by 66 · see citing works
Predicting the Personal Appeal of Marketing Images Using Computational Methods
Matz, S., Segalin, C., Stillwell, D., Müller, S. R., & Bos, M. (2019) · Journal of Consumer Psychology · doi:10.1002/jcpy.1092 · Cited by 53 · see citing works
Self-citationA Path to Mitigating SNS Ad Avoidance: Tailoring Messages to Individual Personality Traits
Dodoo, N. A., & Wen, J. (2019) · Journal of Interactive Advertising · doi:10.1080/15252019.2019.1573159 · Cited by 69 · see citing works
Montag, C., Baumeister, H., Kannen, C., Sariyska, R., Meßner, E.-M., & Brand, M. (2019) · J — Multidisciplinary Scientific Journal · doi:10.3390/j2020008 · Cited by 70 · see citing works
Digital Food Marketing to Young People: A Substantial Public Health Challenge
Boyland, E. J., Thivel, D., Mazur, A., Ring‐Dimitriou, S., Frelut, M., Weghuber, D., & On behalf of the European Childhood Obesity Group (2020) · Annals of Nutrition and Metabolism · doi:10.1159/000506413 · Cited by 64 · see citing works
- 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 › Sociology and Political Science › Psychology of Social Influence
- Social Sciences › Sociology and Political Science › 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.
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