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

Michal Kosinski, David Stillwell, and Thore Graepel
PNAS April 9, 2013 110 (15) 5802-5805; https://doi.org/10.1073/pnas.1218772110

  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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Figures

  • Fig. 1.
    Fig. 1.

    The study is based on a sample of 58,466 volunteers from the United States, obtained through the myPersonality Facebook application (www.mypersonality.org/wiki), which included their Facebook profile information, a list of their Likes (n = 170 Likes per person on average), psychometric test scores, and survey information. Users and their Likes were represented as a sparse user–Like matrix, the entries of which were set to 1 if there existed an association between a user and a Like and 0 otherwise. The dimensionality of the user–Like matrix was reduced using singular-value decomposition (SVD) (24). Numeric variables such as age or intelligence were predicted using a linear regression model, whereas dichotomous variables such as gender or sexual orientation were predicted using logistic regression. In both cases, we applied 10-fold cross-validation and used the k = 100 top SVD components. For sexual orientation, parents’ relationship status, and drug consumption only k = 30 top SVD components were used because of the smaller number of users for which this information was available.

  • Fig. 2.
    Fig. 2.

    Prediction accuracy of classification for dichotomous/dichotomized attributes expressed by the AUC.

  • Fig. 3.
    Fig. 3.

    Prediction accuracy of regression for numeric attributes and traits expressed by the Pearson correlation coefficient between predicted and actual attribute values; all correlations are significant at the P < 0.001 level. The transparent bars indicate the questionnaire’s baseline accuracy, expressed in terms of test–retest reliability.

  • Fig. 4.
    Fig. 4.

    Accuracy of selected predictions as a function of the number of available Likes. Accuracy is expressed as AUC (gender) and Pearson’s correlation coefficient (age and Openness). About 50% of users in this sample had at least 100 Likes and about 20% had at least 250 Likes. Note, that for gender (dichotomous variable) the random guessing baseline corresponds to an AUC = 0.50.

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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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