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Research Article

Private traits and attributes are predictable from digital records of human behavior

Michal Kosinski, David Stillwell, and Thore Graepel
  1. aFree School Lane, The Psychometrics Centre, University of Cambridge, Cambridge CB2 3RQ United Kingdom; and
  2. bMicrosoft Research, Cambridge CB1 2FB, United Kingdom

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PNAS April 9, 2013 110 (15) 5802-5805; https://doi.org/10.1073/pnas.1218772110
Michal Kosinski
aFree School Lane, The Psychometrics Centre, University of Cambridge, Cambridge CB2 3RQ United Kingdom; and
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  • For correspondence: [email protected]
David Stillwell
aFree School Lane, The Psychometrics Centre, University of Cambridge, Cambridge CB2 3RQ United Kingdom; and
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Thore Graepel
bMicrosoft Research, Cambridge CB1 2FB, United Kingdom
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  1. Edited by Kenneth Wachter, University of California, Berkeley, CA, and approved February 12, 2013 (received for review October 29, 2012)

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Abstract

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.

  • social networks
  • computational social science
  • machine learning
  • big data
  • data mining
  • psychological assessment

Footnotes

  • ↵1To whom correspondence should be addressed. E-mail: mk583{at}cam.ac.uk.
  • Author contributions: M.K. and T.G. designed research; M.K. and D.S. performed research; M.K. and T.G. analyzed data; and M.K., D.S., and T.G. wrote the paper.

  • Conflict of interest statement: D.S. received revenue as owner of the myPersonality Facebook application.

  • This article is a PNAS Direct Submission.

  • Data deposition: The data reported in this paper have been deposited in the myPersonality Project database (www.mypersonality.org/wiki).

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1218772110/-/DCSupplemental.

Freely available online through the PNAS open access option.

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Digital records of behavior expose personal traits
Michal Kosinski, David Stillwell, Thore Graepel
Proceedings of the National Academy of Sciences Apr 2013, 110 (15) 5802-5805; DOI: 10.1073/pnas.1218772110

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Digital records of behavior expose personal traits
Michal Kosinski, David Stillwell, Thore Graepel
Proceedings of the National Academy of Sciences Apr 2013, 110 (15) 5802-5805; DOI: 10.1073/pnas.1218772110
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