Predicting personality from patterns of behavior collected with smartphones
| Autor: | Daniel Buschek, Theresa Ullmann, Clemens Stachl, Sarah Theres Völkel, Markus Bühner, Samuel D. Gosling, Gabriella M. Harari, Bernd Bischl, Heinrich Hussmann, Tobias Schuwerk, Quay Au, Michelle Oldemeier, Ramona Schoedel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
media_common.quotation_subject
Internet privacy 050109 social psychology behavioral science 050105 experimental psychology Machine Learning Quantitative Trait Heritable Phone Personality Humans 0501 psychology and cognitive sciences Social media Big Five personality traits Social Behavior media_common Multidisciplinary Point (typography) business.industry 05 social sciences Behavioral pattern Reproducibility of Results Models Theoretical Media consumption Facet (psychology) SI Correction Privacy Smartphone business Psychology social sciences |
| Zdroj: | Proc Natl Acad Sci U S A |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
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