Predicting personality from patterns of behavior collected with smartphones
Journal
Proceedings of the National Academy of Sciences
Type
journal article
Date Issued
2020
Author(s)
;
Au, Q.
;
Schoedel, R.
;
Gosling, S. D
;
Harari, G. M.
;
Buschek, D.
;
Völkel, S. T.
;
Schuwerk, T.
;
Oldemeier, M.
;
Ullmann, T.
;
Hussmann, H.
;
Bischl, B.
;
Bühner, M.
Abstract
Smartphones enjoy high adoption rates around the globe. Rarely more than an arm’s length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users’ behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals’ Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain (rmedian
= 0.37) and narrow facet levels (rmedian
= 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals’ private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.
= 0.37) and narrow facet levels (rmedian
= 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals’ private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.
Language
English
Refereed
Yes
Volume
117
Number
30
Start page
17680
End page
17687
Official URL
Subject(s)
Division(s)
Eprints ID
264542
File(s)![Thumbnail Image]()
Name
17680.full.pdf
Size
1007.67 KB
Format
Adobe PDF
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