Predicting personality from patterns of behavior collected with smartphones

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 their users' behaviors (e.g., location, communication, media con-sumption), posing serious threats to individual privacy. Here 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 har-vested from smartphones. Taking a machine-learning approach, we predict personality at broad domain (r(median) = 0.37) and nar-row facet levels (r(median) = 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 behaviors in the domains of 1) communication and social behav-ior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day-and night-time activity are distinc-tively 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 demon-strates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the bene-fits (e.g., in research settings) and dangers (e.g., privacy impli-cations, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.