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.

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For decades, day–night patterns in behaviour have been investigated by asking people about their sleep–wake timing, their diurnal activity patterns, and their sleep duration. We demonstrate that the increasing digitalization of lifestyle offers new possibilities for research to investigate day–night patterns and related traits with the help of behavioural data. Using smartphone sensing, we collected in vivo data from 597 participants across several weeks and extracted behavioural day–night pattern indicators. Using this data, we explored three popular research topics. First, we focused on individual differences in day–night patterns by investigating whether ‘morning larks’ and ‘night owls’ manifest in smartphone‐sensed behavioural indicators. Second, we examined whether personality traits are related to day–night patterns. Finally, exploring social jetlag, we investigated whether traits and work weekly day–night behaviours influence day–night patterns on weekends. Our findings highlight that behavioural data play an essential role in understanding daily routines and their relations to personality traits. We discuss how psychological research can integrate new behavioural approaches to study personality.

Abstract. The increasing usage of new technologies implies changes for personality research. First, human behavior becomes measurable by digital data, and second, digital manifestations to some extent replace conventional behavior in the analog world. This offers the opportunity to investigate personality traits by means of digital footprints. In this context, the investigation of the personality trait sensation seeking attracted our attention as objective behavioral correlates have been missing so far. By collecting behavioral markers (e.g., communication or app usage) via Android smartphones, we examined whether self-reported sensation seeking scores can be reliably predicted. Overall, 260 subjects participated in our 30-day real-life data logging study. Using a machine learning approach, we evaluated cross-validated model fit based on how accurate sensation seeking scores can be predicted in unseen samples. Our findings highlight the potential of mobile sensing techniques in personality research and show exemplarily how prediction approaches can help to foster an increased understanding of human behavior.

We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification versions of randomForestSRC and rFerns. All these methods can be easily compared by different implemented multilabel performance measures and resampling methods in the standardized mlr framework. In a benchmark experiment with several multilabel datasets, the performance of the different methods is evaluated.