Predicting Personality Traits Using Smartphone Sensor Data and App Usage Data

Abstract

Human behavior is complex - often defying explanation using traditional mathematical models. Intermediate forms, such as those gleaned from personality inventories can be employed to predict stylized aspects of behavior. Novel sensing systems have made tracking behavior possible with unprecedented fidelity, leading to the hypothesis that intermediate psychological models might be derived directly from records of behavior. Being able to extract a personal profile or personality type from data directly obtainable from a mobile phone without participant interaction could have applications for marketing, or for initiating social or health interventions. In this paper, we model a well-established personality inventory, the Big Five framework. Daily routines of participants were measured from parameters readily available from smartphones and supervised machine learning was used to create a model from that data.

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