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.

The understanding, quantification and evaluation of individual differences in behavior, feelings and thoughts have always been central topics in psychological science. An enormous amount of previous work on individual differences in behavior is exclusively based on data from self-report questionnaires. To date, little is known about how individuals actually differ in their objectively quantifiable behaviors and how differences in these behaviors relate to big five personality traits. Technological advances in mobile computer and sensing technology have now created the possiblity to automatically record large amounts of data about humans' natural behavior. The collection and analysis of these records makes it possible to analyze and quantify behavioral differences at unprecedented scale and efficiency. In this study, we analyzed behavioral data obtained from 743 participants in 30 consecutive days of smartphone sensing (25,347,089 logging-events). We computed variables (15,692) about individual behavior from five semantic categories (communication & social behavior, music listening behavior, app usage behavior, mobility, and general day- & nighttime activity). Using a machine learning approach (random forest, elastic net), we show how these variables can be used to predict self-assessments of the big five personality traits at the factor and facet level. Our results reveal distinct behavioral patterns that proved to be differentially-predictive of big five personality traits. Overall, this paper shows how a combination of rich behavioral data obtained with smartphone sensing and the use of machine learning techniques can help to advance personality research and can inform both practitioners and researchers about the different behavioral patterns of personality.

Practically all user activities on a smartphone depend on self-contained software applications, so-called apps. Due to the large number and diversity of available apps, the analysis of app usage behaviour in social science research requires elaborate preprocessing of app data. Therefore, we present a categorisation scheme and a dataset of 3,091 manually categorised apps used by a representative quota sample within a large-scale smartphone sensing study conducted in Germany over several months in 2020. For the categorisation, we report values for inter-rater agreement between two independent raters. We provide the freely available dataset as a CSV and we invite other researchers to use and modify the categorisation for their specific research questions and to extend it for the mobile sensing research community.

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