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Differential ability of network and natural language information on social media to predict interpersonal and mental health traits
Author(s) -
Mori Kazuma,
Haruno Masahiko
Publication year - 2021
Publication title -
journal of personality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.082
H-Index - 144
eISSN - 1467-6494
pISSN - 0022-3506
DOI - 10.1111/jopy.12578
Subject(s) - big five personality traits , psychology , personality , social media , interpersonal communication , set (abstract data type) , mental health , social psychology , cognitive psychology , computer science , world wide web , psychotherapist , programming language
Objective Previous studies have shown that digital footprints (mainly Social Networking Services, or SNS) can predict personality traits centered on the Big Five. 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 23 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.