In Journal of personality ; h5-index 43.0
OBJECTIVE : Previous studies have shown that digital footprints (mainly Social Networking Services, or SNS) can predict personality traits centered on the Big 5. The present study investigates to what extent different types of SNS information predicts wider traits and attributes.
METHOD : We collected an intensive set of 24 (52 subscales) personality traits and attributes (N=239) and examined whether machine learning models trained on four different types of SNS (i.e., Twitter) information (network, time, word statistics, and bag of words) predict the traits and attributes.
RESULTS : We found that four types of SNS information can predict 24 subscales collectively. Furthermore, we validated our hypothesis that the network and word statistics information, respectively, exhibit unique strengths for the prediction of inter-personal traits such as autism and mental health traits such as schizophrenia and anxiety. We also found that intelligence is predicted by all four types of SNS information.
CONCLUSIONS : These results reveal that the different types of SNS information can collectivity predict wider human traits and attributes than previously recognized, and also that each information type has unique predictive strengths for specific traits and attributes, suggesting that personality prediction from SNS is a powerful tool for both personality psychology and information technology.
Mori Kazuma, Haruno Masahiko
SNS, component-wise gradient boosting, machine learning, natural language processing, network, personality traits, prediction, psychiatric disorders, time information