Journal of personality · 2023
Predicting Big Five personality traits from smartphone data: A meta-analysis on the potential of digital phenotyping.
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Marengo Davide, Elhai Jon D, Montag Christian
Аннотация
OBJECTIVE: Since the first study linking recorded smartphone variables to self-reported personality in 2011, many additional studies have been published investigating this association. In the present meta-analyses, we aimed to understand how strongly personality can be predicted via smartphone data.
METHOD: Meta-analytical calculations were used to assess the association between smartphone data and Big Five traits. Because of the lack of independence of many included studies, analyses were performed using a multilevel approach.
RESULTS: Based on data collected from 21 distinct studies, extraversion showed the largest association with the digital footprints derived from smartphone data (r = .35), while remaining traits showed smaller associations (ranging from 0.23 to 0.25). For all traits except neuroticism, moderator analyses showed that prediction performance was improved when multiple features were combined together in a single predictive model. Additionally, the strength of the prediction of extraversion was improved when call and text log data were used to perform the prediction, as opposed to other types of smartphone data CONCLUSIONS: Our synthesis reveals small-to-moderate associations between smartphone activity data and Big Five traits. The opportunities, but also dangers of the digital phenotyping of personality traits based on traces of users' activity on a smartphone data are discussed.
METHOD: Meta-analytical calculations were used to assess the association between smartphone data and Big Five traits. Because of the lack of independence of many included studies, analyses were performed using a multilevel approach.
RESULTS: Based on data collected from 21 distinct studies, extraversion showed the largest association with the digital footprints derived from smartphone data (r = .35), while remaining traits showed smaller associations (ranging from 0.23 to 0.25). For all traits except neuroticism, moderator analyses showed that prediction performance was improved when multiple features were combined together in a single predictive model. Additionally, the strength of the prediction of extraversion was improved when call and text log data were used to perform the prediction, as opposed to other types of smartphone data CONCLUSIONS: Our synthesis reveals small-to-moderate associations between smartphone activity data and Big Five traits. The opportunities, but also dangers of the digital phenotyping of personality traits based on traces of users' activity on a smartphone data are discussed.