Predicting personality from patterns of behavior collected with smartphones
Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D Gosling, Gabriella M Harari, Daniel Buschek, Sarah Theres Voelkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus Buehner
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | NATL ACAD SCIENCES | Published : 2021
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 ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on be..View full abstract
Awarded by German Federal Ministry of Education and Research Grant
Awarded by National Science Foundation
We thank L. Sust, F. Bemmann, M. Schiele, A. Dietl, V. Gebhardt, S. Huber, M. Frohlich, M. Metz, F. Lehmann, P. A. Vu, D. Becker, J. Kaiser, P. Ehrich, A. De Luca, and M. Lamm for help with recruitment, programming, and app testing. We thank the Schuhfried GmbH for providing the BFSI. We thank F. Pargent, J. Rauthmann, F. Schonbrodt, and A. Bender for insightful comments on earlier versions of the manuscript. We thank N. Zschack for her help with data visualization. Finally, we thank the reviewers for their constructive suggestions. This project was partially funded by the Bavarian State Ministry of Science and the Arts in the framework of the Center Digitisation.Bavaria, a Google research grant, the LMU-excellence initiative, the German Federal Ministry of Education and Research Grant 01IS18036A, and the National Science Foundation Award SES-1758835.