I employ big data and computational models to address pressing issues, including privacy risks, psychometrics, online mass persuasion, and psychological profiling. In pursuing my research, I have published over 70 papers, which have been cited over 11,000 times. The following subsections summarize my work and its implications.
A list of all of my papers can be found in my CV.
Impact:
Kulkarni, V., Kern, M.L., Stillwell, D., Kosinski, M., Matz, S., Ungar, L., Skiena, S., & Schwartz, H.A. (2018). Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach. PLOS ONE.
Mo, F., Zhou, J., Kosinski, M., & Stillwell, D. (2018). Usage patterns and social circles on Facebook among elderly people with diverse personality traits. Educational Gerontology.
Segalin, C., Celli, F., Polonio, L., Kosinski, M., Stillwell, D., Sebe, N., … Lepri, B. (2017). What your Facebook profile picture reveals about your personality. Proceedings of the 2017 ACM on Multimedia Conference.
Rohrer, J., Egloff, B., Schmukle, S., Stillwell, D., & Kosinski, M. (2017). In Your Eyes Only? Discrepancies and Agreement Between Self- and Other-Reports of Personality From Age 14 to 29. Journal of Personality and Social Psychology.
Matz, S., Chan, F., & Kosinski, M. (2016). Models of Personality. In M. Tkalčič (Ed.), Emotions and Personality in Personalized Systems.
Yaden, D., Park, G., Schwartz, A., Kern, M.L., Eichstaedt, J.C., Kosinski, M., … Seligman, M.E.P. (2016). Women are Warmer but No Less Assertive Than Men: Gender and Language on Facebook. PLOS ONE.
Lambiotte, R., & Kosinski, M. (2015). Tracking the Digital Footprints of Personality. Proceedings of the Institute of Electrical and Electronics Engineers.
Aghaee, S., Blackwell, A., Kosinski, M., & Stillwell, D. (2015). Personality and Intrinsic Motivational Factors in End-User Programming. Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing.
Mahalingam, V., Stillwell, D., Kosinski, M., Rust, J., & Kogan, A. (2013). Who Can Wait for the Future? A Personality Perspective. Social Psychological and Personality Science.
Cantador, I., Fernandez-Tobias, I., Bellogin, A., Kosinski, M., & Stillwell, D. J. (2013). Relating Personality Types with User Preferences in Multiple Entertainment Domains. Proceedings of the 1st Workshop on Emotions and Personality in Personalized Services.
Schwartz, H. A., Eichstaedt, J. C., Dziurzynski, L., Kern, M. L., Blanco, E., Kosinski, M., ... Ungar, L. H. (2013). Toward Personality Insights from Language Exploration in Social Media. Proceedings of the AAAI Spring Symposium Series. [Also see poster.]
Kern, M. L., Eichstaedt, J. C., Schwartz, H. A., Dziurzynski, L., Ungar, L. H., Kosinski, M., ... Seligman, M. E. P. (2013). The Online Social Self: An Open Vocabulary Approach to Personality. Assessment.
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Kosinski, M.... Ungar, L. H. (2013). Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLOS ONE.
Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D. J., & Graepel, T. (2013). Manifestations Of User Personality In Website Choice And Behaviour On Online Social Networks. Machine Learning.
Kosinski, M., Kohli, P., Stillwell, D. J., Bachrach, Y., & Graepel, T. (2012). Personality and Website Choice. Proceedings of the ACM Web Science Conference (WebSci).
Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P. & Stillwell, D. J. (2012). Personality and Patterns of Facebook Usage. Proceedings of the ACM Web Science Conference (WebSci).
Quercia, D., Lambiotte, R., Stillwell, D., Kosinski, M., & Crowcroft, J. The Personality of Popular Facebook Users. Proceedings of the ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW).
Quercia, D., Kosinski, M., Stillwell, D. J., & Crowcroft, J. (2011). Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. Proceedings of the IEEE International Conference on Privacy, Security, Risk and Trust, and the IEEE International Conference on Social Computing.
The theoretical foundations of these links are well established. Faces shape others’ perceptions and expectations that, in turn, shape our behavior and psychological traits. Psychological traits and facial features are both influenced by many common factors, such as hormones and genes. Finally, psychological traits influence facial appearance; for example, attitudes affect group memberships that, in turn, influence facial grooming style.
Both old wives’ tales and psychological literature posit that spouses’ faces become more similar over time. Surprisingly, this widely accepted belief is supported by a single 1987 study of 12 couples, whose results have never been replicated. We validate it in a large sample of 517 couples using two independent methods of estimating facial similarity (human judgment and a state-of-the-art facial recognition algorithm). Our results show that while spouses’ faces tend to be similar at the beginning of marriage, they do not converge over time.
Impact:
The most impactful paper of similar age published in Nature's Scientific Reports
Many studies have linked facial width-to-height ratio (fWHR) with various antisocial and violent behavioral tendencies. However, those studies have predominantly been laboratory-based and low-powered. This paper showed that the links between fWHR and psychological traits were weak, and that their patterns were inconsistent with the previous findings and theoretical assumptions advanced in the fWHR literature.
Wang, Y., Lakkaraju, H., Kosinski, M., & Leskovec, J. (2016). Psycho-Demographic Analysis of the Facebook Rainbow Campaign. ArXiv (preprint).
Ubiquitous facial recognition technology can expose individuals’ political orientation, as faces of liberals and conservatives consistently differ. A facial recognition algorithm was applied to naturalistic images of over 1 million individuals to predict their political orientation by comparing their similarity to faces of liberal and conservative others. Political orientation was correctly classified in 72% of liberal–conservative face pairs, remarkably better than chance (50%), human accuracy (55%), or one afforded by a 100-item personality questionnaire (66%). Accuracy was similar across countries (the U.S., Canada, and the UK), environments (Facebook and dating websites), and when comparing faces across samples. Accuracy remained high (69%) even when controlling for age, gender, and ethnicity. Given the widespread use of facial recognition, our findings have critical implications for the protection of privacy and civil liberties.
Impact:
After discovering that governments and corporations had been using facial analysis algorithms to detect intimate traits, we decided to examine the privacy threats posed by such algorithms in the context of one of the most sensitive personal characteristics: sexual orientation.
We showed that an algorithm can detect sexual orientation from facial images with an accuracy comparable with that of mammograms or modern diagnostic tools for Parkinson's disease. The differences between the faces of gay and straight people were consistent with widely accepted theories explaining the origins of sexual orientation.
The study has been successfully replicated by two research teams using independently collected data:
Impact:
The privacy threats highlighted in this paper inspired the cover and the leading article of The Economist (09/09/2017). See the cover story and the science section.
This paper illustrated the feasibility and effectiveness of the mass persuasion techniques deployed by companies such as Cambridge Analytica and exposed the risks inherent to behavioral targeting, the principal source of income for many tech giants.
Impact:
See also:
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2018). Reply to Sharp et al.: Psychological targeting produces robust effects. Proceedings of the National Academy of Sciences.
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2018). Reply to Eckles et al.: Facebook’s optimization algorithms are highly unlikely to explain the effects of psychological targeting. Proceedings of the National Academy of Sciences.
Upon realizing the extent to which companies and governments use people's digital footprints to extract their intimate traits, we decided to see how accurate such predictions are using one of the most pervasive and easily accessible digital footprints, Facebook Likes. Our results showed that they can be used to automatically and accurately predict a range of sensitive traits, including sexual orientation, ethnicity, political views, personality, intelligence, use of addictive substances, parental separation, etc.
This paper and accompanying op-ed in Financial Times called for, and triggered, an immediate tightening of Facebook’s privacy policies: Two weeks after its publication, Facebook switched off public access to its users’ Likes (until then, they were by default visible to anyone on the internet). This greatly limited exposure to privacy threats for millions of users and reduced the damage caused by Cambridge Analytica.
Impact:
Second most impactful paper of 2013 according to Altmetrics.com
Most cited paper published by PNAS in 2013 (category: Psychology and Cognitive Sciences)
Inspired two TED Talks: by David Stillwell and Jenn Golbeck (who, by the way, had nothing to do with this paper)
Bachrach, Y., Graepel, T., Kohli, P., Kosinski, M., & Stillwell, D. J. (2014). Your Digital Image: Factors Behind Demographic And Psychometric Predictions From Social Network Profiles. Proceedings of the 2014 International Conference on Autonomous Agents and Multiagent Systems.
Bi, B., Kosinski, M., Shokouhi, M., & Graepel, T. (2013). Inferring the Demographics of Search Users. Proceedings of 22nd International World Wide Web Conference.
Quercia, D., Las, D., Jo, C., David, P., Kosinski, M., Almeida, V., & Crowcroft, J. (2012). Facebook and Privacy: The Balancing Act of Personality, Gender, and Relationship Currency. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media.
"There is a robust science for predicting and explaining what people do in any area of life, and this remarkable book, by three leading scholars, will forever change the way you think about human behavior: a true masterpiece!" – Tomas Chamorro-Premuzic, Columbia University, USA and University College, UK.
"Measurement is the foundation of all science, and Psychology is no exception. So, with its authoritative, updated, and comprehensive coverage of psychometrics, this volume is set to become the go-to guide for any serious psychological scientist." – Sam Gosling, University of Texas, USA.
"The science of psychometrics is already changing our lives. For better or worse, it will shape our digital futures. This welcome new edition to the classic introduction to the field could hardly be more timely. " – Huw Price, University of Cambridge, UK.
Download the preprint of my chapter: https://psyarxiv.com/tkhg4
This volume -- an update to the original, 2010 edition -- provides a graduate-level introduction to social psychology. The target audience consists of first-year graduate students (MA or PhD) in social psychology and related disciplines (marketing, organizational behavior, etc.), although it is also appropriate for upper-level undergraduate courses. The authors are world-renowned leaders on their topic, and they have written state-of-the-art overviews of the discipline's major research domains. The chapters are not only scientifically rigorous, but also accessible and engaging. They convey the joy, excitement, and promise of scientific investigations into human sociality.
Kosinski, M., & Behrend, T. (2017). Editorial overview: Big data in the behavioral sciences. Current Opinion in Behavioral Sciences.
Inkster, B., Stillwell, D. J., Kosinski, M., & Jones, P. B. (2016). A decade into Facebook: where is psychiatry in the digital age? The Lancet Psychiatry.
Kosinski, M., Matz, S., Gosling, S. D., Popov, V., & Stillwell, D. J. (2016). Facebook as a Research Tool. Monitor on Psychology.
Mahalingam, V., Palkovic, M., Kosinski, M., & Stillwell, D. (2016). A computer adaptive measure of delay discounting. Assessment.
Wilmot, M.P., Kostal, J., Stillwell, D. J., & Kosinski, M. (2015). Using Item Response Theory to Develop Measures of Acquisitive and Protective Self-Monitoring from the Original Self-Monitoring Scale. Assessment.
Farnadi, G., Sitaraman, G., Sushmita, S., Celli, F., Kosinski, M., Stillwell, D., … De Cock, M. (2015). Computational Personality Recognition in Social Media. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI).
Sap, M., Park, G., Eichstaedt, J. C., Kern, M. L., Stillwell, D. J., Kosinski, M., … Schwartz, H. A. (2014). Developing Age and Gender Predictive Lexica over Social Media. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., … Seligman, M. E. P. (2014). Automatic Personality Assessment through Social Media Language. Journal of Personality and Social Psychology (JPSP).
Rife, S. C., Cate, K. L., Kosinski, M., & Stillwell, D. J. (2014). Participant recruitment and data collection through Facebook: the role of personality factors. International Journal of Social Research Methodology (IJSRM).
Han, K. T., & Kosinski, M. (2013). Software Tools for Multistage Testing Simulations. In D. Yan, C. Lewis, & A. von Davier (Eds.), Computerized Multistage Testing: Theory and Applications.
AERA Division D Significant Contributions Award
Celli, F., Pianesi, F., Stillwell, D. J., & Kosinski, M. (2013). Workshop on Computational Personality Recognition. Proceedings of the AAAI International Conference on Weblogs and Social Media (ICWSM).
Markovikj, D., Gievska, S., Kosinski, M., & Stillwell, D. J. (2013). Mining Facebook Data for Predictive Personality Modeling. Proceedings of the AAAI International Conference on Weblogs and Social Media (ICWSM).
Stillwell, D. J., & Kosinski, M. (2012). myPersonality project: Example of successful utilization of online social networks for large-scale social research. Proceedings of the ACM Workshop on Mobile Systems for Computational Social Science (MobiSys).
Chen, L., Gong, T., Kosinski, M., Stillwell, D. J., & Davidson, R. L. (2017). Building a profile of subjective well-being for social media users. PLOS ONE.
Schwartz, H. A., Sap, M., Kern, M. L., Eichstaedt, J. C., Kapelner, A., Agrawal, M., … Kosinski, M. (2016). Predicting Individual Well-Being Through the Language of Social Media. Pacific Symposium on Biocomputing.
Liu, P., Tov, W., Kosinski, M., Stillwell, D. J., & Qiu, L. (2015). Do Facebook Status Updates Reflect Subjective Well-being? Valence and Time Matter. Cyberpsychology, Behavior, and Social Networking.
Collins, S., Sun, Y., Kosinski, M., Stillwell, D. J., & Markuzon, N. (2015). Are You Satisfied with Life?: Predicting Satisfaction with Life from Facebook. Proceedings of the International Social Computing, Proceedings of Behavioral Modeling and Prediction Conference.
Farnadi, G., Sitaraman, G., Rohani, M., Kosinski, M., Stillwell, D. J., Moens, M.-F., … De Cock, M. (2014). How are you doing? Emotions and Personality in Facebook. Proceedings of the International Conference on User Modelling, Adaptation and Personalization (UMAP).
Wang, N., Kosinski, M., Stillwell, D. J., & Rust, J. (2014). Can Well-Being be Measured Using Facebook Status Updates? Validation of Facebook’s Gross National Happiness Index. Social Indicators Research.
Nave, G., Minxha, J., Greenberg, D. M., Kosinski, M., Stillwell, D. J. & Rentfrow, J. (2018). Musical Preferences Predict Personality: Evidence from Active Listening and Facebook Likes. Psychological Science.
Bonneville-Roussy, A., Stillwell, D., Kosinski, M., & Rust, J. (2017). Age trends in musical preferences in adulthood: Conceptualization and empirical investigation. Musicae Scientiae.
Greenberg, D. M., Kosinski, M., Stillwell, D. J., Monteiro, B. L., Levitin, D. J., & Rentfrow P. J. (2016). The Song Is You: Preferential Reactions to Musical Attribute Dimensions Reflect Personality. Social Psychological and Personality Science.
Greenberg, D. M., Baron-Cohen, S., Stillwell, D. J., Kosinski, M., & Rentfrow, P. J. (2015). Musical Preferences are Linked to Cognitive Styles. PLOS ONE.
Rentfrow, P. J., Goldberg, L. R., Stillwell, D. J., Kosinski, M., Gosling, S. D., & Levitin, D. J. (2012). The Song Remains the Same: A Replication and Extension of the MUSIC Model. Music Perception.
Garcia, D., Drejing, K., Amato, C., Kosinski, M., & Sikström, S. (2018). The Promotion of a Bright Future and the Prevention of a Dark Future: Time Anchored Incitements in News Articles and Facebook's Status Updates. Frontiers of Psychology.
Gil-Lopez, T., Shen, C., Benefield, G. A., Palomares, N. A., Kosinski, M., & Stillwell, D. J. (2018). One Size Fits All: Context Collapse, Self-Presentation Strategies and Language Styles on Facebook. Journal of Computer-Mediated Communication.
Yaden, D., Eichstaedt, J. C., Kern, M.L., Smith, L. Bufonne, A., Stillwell, D. J., … Kosinski, M. (2017). The Language of Religious Affiliation: Social, Emotional, and Cognitive Difference. Social Psychological and Personality Science.
Mao, M., Stillwell, D. J., Kosinski, M., & Good D. (2017). Testing Ageing Theory among Later Middle-aged and Older Users Using Social Media Authors. Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing.
Feldman, G., Lian H., Kosinski, M., & Stillwell, D. (2016). Frankly, we do give a damn: The relationship between profanity and honesty. Social Psychological and Personality Science.
Mao, M., Stillwell, D. J., Kosinski, M., & Good D. (2016). Age Differences in Social Media: Testing Continuity and Activity Theory among Later Middle-aged and Older Facebook Users. Proceedings of Human-Computer Interaction Conference (CHI).
Gillan, C.M., Kosinski, M., Whelan, R., Phelps, E. A., & Daw, N. D. (2016). Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. eLife.
Park, G., Schwartz, H.A., Sap, M., Kern, M. L., Weingarten, E., Eichstaedt, J., … Kosinski M. (2015). Living in the Past, Present, and Future: Measuring Temporal Orientation with Language. Journal of Personality.
Schwartz, A., Park, G. J. Sap, M., Weingarten, E., Eichstaedt, J., Kern, M. L., … Kosinski, M. (2015). Extracting Human Temporal Orientation through Facebook. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).
Boyd, R. L., Wilson, S. R., Pennebaker, J. W., Kosinski, M., Stillwell, D. J., & Mihalcea, R. (2015). Values in Words: Using Language to Evaluate and Understand Personal Values. Proceedings of the Ninth International AAAI Conference on Web and Social Media.
Wilmot, M. P., DeYoung, C. G., Stillwell, D. J., & Kosinski, M. (2015). Self-Monitoring and the Metatraits. Journal of Personality.
Na, J., Kosinski, M., & Stillwell, D. (2015). When a new tool is introduced in different cultural contexts: Individualism-Collectivism and social network on Facebook. Journal of Cross-Cultural Psychology.
He, Q., Glas, C., Kosinski, M., Stillwell, D. J., & Veldkamp, B. P. (2014). Predicting self-monitoring skills using textual posts on Facebook. Computers in Human Behavior.
Rentfrow, P. J., Gosling, S. D., Jokela, M., Stillwell, D. J., Kosinski, M., & Potter, J. (2013). Divided We Stand: Three Psychological Regions of the United States and their Political, Economic, Social, and Health Correlates. Journal of Personality and Social Psychology (JPSP).
Kern, M. L., Eichstaedt, J. C., Schwartz, H. A., Park, G., Ungar, L. H., Stillwell, D. J., … Kosinski, M. From "sooo excited!!!" to "so proud": Using Language to Study Development. Developmental Psychology.
Friggeri, A., Lambiotte, R., Kosinski, M., & Fleury, E. (2012). Psychological Aspects of Social Communities. Proceedings of the International Conference on Privacy, Security, Risk and Trust and the IEEE International Conference on Social Computing.
Kosinski, M., Bachrach, Y., Kasneci, G., Van Gael, J., & Graepel, T. (2012). Crowd IQ: Measuring the Intelligence of Crowdsourcing Platforms. Proceedings of the ACM Web Science Conference (WebSci).
Kosinski, M., Bachrach, Y., Graepel, T., Kasneci, G., & Van Gael, J. (2012). Crowd IQ - Aggregating Opinions to Boost Performance. Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). [View poster]
Measurement and prediction of individual and group differences in the digital environment by M. Kosinski, Ph.D. Dissertation, University of Cambridge, 2014.
Application of the dominance and ideal point IRT models to the Extraversion scale from the IPIP Big Five Personality Questionnaire by M. Kosinski, MPhil dissertation, University of Cambridge, 2009.
Protocol validity indices in the sample from an on-line personality questionnaire by M. Kosinski, Technical Report, Cambridge University, 2009.
On-line auctions: The influence of the Buy-It-Now price on the auction outcome by M. Kosinski, MA dissertation, Warsaw School of Social Sciences (SWPS), 2008.