Detecting Personality Unobtrusively from Users' Online and Offline Workplace Behaviors

摘要

Personality affects various social behaviors of an individual, such as collaboration, group dynamics, and social relationships within the workplace. However, existing methods for assessing personality have shortcomings: self-assessed methods are cumbersome due to repeated assessment and erroneous due to a self-report bias. On the other hand, automatic, data-driven personality detection raises privacy concerns due to a need for excessive personal data. We present an unobtrusive method for detecting personality within the workplace that combines a user's online and offline behaviors. We report insights from analyzing data collected from four different workplaces with 37 participants, which shows that complementing online and offline data allows a more complete reflection of an individual's personality. We also present possible applications of unobtrusive personality detection in the workplace.