Private traits and attributes are predictable from digital records of human behavior
Open accessAbstract
We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.
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The larger one's digital footprint, the easier it is for a simple algorithm to ascertain one's personal and private attributes.
“Indeed, the larger one's digital footprint, the easier it is for a simple algorithm to ascertain one's personal and private attributes (Kosinski et al., 2013).”
From The Child Labor in Social Media: Kidfluencers, Ethics of Care, and Exploitation (2025) · cites this paper as Kosinski et al., 2013
People's consumption behavior is correlated with their demographic attributes and other personal characteristics.
“Further, people's consumption behavior is correlated with their demographic attributes and other personal characteristics: Kosinski et al. (2013) links “likes” on Facebook with personal traits, while web page views vary across demographics, e.g., age, gender, race, income (Goel et al., 2012).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Kosinski et al., 2013
The personality observed in status updates was rated using Apply Magic Sauce, a validated research API for measuring personality in open-ended text.
“The personality observed in the status updates generated for each simulated human profile was then rated using Apply Magic Sauce77, a validated research application programming interface (API) for measuring personality in open-ended text.”
From A psychometric framework for evaluating and shaping personality traits in large language models (2025) · cites this paper as [77]
The paper’s Abstract describes predicting personality from Facebook Likes and measuring personality with an IPIP questionnaire; the Acknowledgements link to a demonstration based on Likes. Searches for Apply Magic Sauce, an API, and personality measurement from status-update text found no passage establishing that this paper describes or validates the cited tool. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIt can be possible to infer highly sensitive and private information about individuals on the basis of social media data.
“it can be possible to infer highly sensitive and private information about individuals on the basis of social media data [50].”
From The growing ubiquity of algorithms in society: implications, impacts and innovations (2018) · cites this paper as [50]
Information that is not directly observed can be deduced from what has already been shared, especially if multiple data breaches concern the same individual.
“Another natural concern is the spread of the information that is not directly observed, but is deducible from what has already been shared [32,50,97], especially if multiple data breaches concern the same individual.”
From The growing ubiquity of algorithms in society: implications, impacts and innovations (2018) · cites this paper as [50]
Machine learning models can estimate social media users' personal attributes with surprising accuracy; one model correctly identified Democrat or Republican 85% of the time.
“For example, machine learning models can estimate social media users' personal attributes with surprising accuracy (under one model, correctly identifying Democrat or Republican 85% of the time [35]);”
From Two social lives: how differences between online and offline interaction influence social outcomes (2020) · cites this paper as [35]
The personality questionnaire has a retest reliability of [12].
“Participants could take 20 to 100 item versions of the questionnaire, with a retest reliability of [12].”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [12]
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- Authors
- Kosinski, M., Stillwell, D., & Graepel, T.
- Affiliations
- Free School Lane, The Psychometrics Centre, University of Cambridge, Cambridge CB2 3RQ United Kingdom; and
- University of Cambridge (GB)
- Microsoft Research (United Kingdom) (GB)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-03-11
- Volume
- 110
- Issue
- 15
- Pages
- 5802-5805
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3625324
- OpenAlex
- W2153803020
- Cited by
- 2,829
- References
- 34
- Citations by year
- 2026: 125 · 2025: 130 · 2024: 186 · 2023: 221 · 2022: 255 · 2021: 299 · 2020: 370 · 2019: 252 · 2018: 289 · 2017: 208 · 2016: 174 · 2015: 145 · 2014: 121 · 2013: 50 · 2012: 2
- FWCI
- 504.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Microsoft Research
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- Facebook likes, personality prediction, sexual orientation, ethnicity, political attitudes, religious beliefs, intelligence, happiness, substance use, parental separation, web personalization, privacy, dimensionality reduction
- MeSH terms
- Artificial Intelligence, Behavior, Emotions, Female, Homosexuality, Humans, Male, Models, Theoretical, Personality, Personality Inventory, Politics, Psychometrics, Regression Analysis, Social Support, Reproducibility of Results, Heterosexuality, Data Mining
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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