Abstract In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and self-reported Big-Five personality traits (extraversion, agreeableness, conscientiousness, emotional stability and openness to experience). Our data stem from smartphones of 117 Nokia N95 smartphone users, collected over a continuous period of 17 months in Switzerland. From the analysis, we show that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits. Next, we describe a machine learning method to detect the personality trait of a user based on smartphone usage. Finally, we study the benefits of using gender-specific models for this task. Apart from a psychological viewpoint, this study facilitates further research on the automated classification and usage of personality traits for personalizing services on smartphones.
Authors of REF have shown that aggregated features obtained from smartphone usage data (e.g., app usage, phone call records, location information, etc) are indicators of the Big-Five personality traits (i.e., extraversion, agreeableness, conscientiousness, emotional stability and openness to experience).
Mining large-scale smartphone data for personality studies
Personal and Ubiquitous Computing
Personal and Ubiquitous Computing
Computer Science