Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

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Abstract:
Digital Phenotyping (DP) entails exploring digital expressions of human personality using behavioral cues drawn from smartphones’ digital footprints. Most personality-oriented DP studies focus narrowly on the Big5 model. This research aims to broaden this approach, using fifty-four personality constructs rooted in fifteen leading personality theories beyond the Big5. Our sample consisted of 104 respondents from whom smartphone data was collected over 7-10 days. We implemented both deductive (hypothesis-testing) and inductive (machine learning) modelling methods. Results show that fifteen of the sixteen broad personality constructs were successfully predicted from smartphone data (forty-eight sub-personality items of the fifty-nine types and personality traits). The best overall predictive model was Gradient Boosted Trees with communication-related features having the highest predictive weight. DP has the potential to transform the field of personality research and may be applied in areas such as HR analytics, personality-based targeted marketing, individualized homeland security, financial risk assessments, personalized medicine, and more.

Bio:
Maya Hocherman Basre is a M.Sc. student at the Department of Industrial Engineering at Tel Aviv University, specializing in business intelligence. Maya holds a B.Sc. degree in Industrial Engineering from Tel Aviv University. During her studies, Maya worked at Sony Semiconductor as a PMO and currently works at Check Point as a System Information Project Manager.

Contact:

E-Mail: mayahoch@gmail.com

Linkedin: https://www.linkedin.com/in/maya-hocherman-83391315a/