Prediction of problematic social media use (PSU) using machine learning approaches

Abstract

In this study, problematic social media use (PSU) was modeled using machine learning with artificial neural networks (ANN) and support vector machines (SVM). Fifteen predictor variables were examined in predicting PSU, including social media usage habits (frequency of daily social media use, history of social media usage, frequency of checking social media accounts, number of shares on social media, and number of social media accounts), desire for being liked, envy of the life of others, narcissistic personality traits (exhibitionism, grandiose fantasies, manipulativeness, thrill-seeking, narcissistic admiration and narcissistic rivalry), fear of missing out (FOMO), and online socialization. The present study comprised 309 (208 females and 101 males) university students. Using ANN and SVM, estimation was performed using k-folds (k = 5) cross validation. Results demonstrated a large relationship between predictors and PSU scores. Estimation rates with ANN and SVM were each .61. Then we used forward selection procedures to determine variable importance. We found that frequency of daily social media use, frequency of checking social media accounts, desire for being liked, exhibitionism and FOMO were the five most important variables in association with PSU severity. Finally, we analyzed the extent to which these five variables predicted PSU, finding that the estimate with five variables had a higher coefficient of estimation than with the fifteen variables. Prediction rates for the five variables were .62 using ANN and .63 using SVM. Results demonstrate that several psychological and social media-related variables were important in modeling PSU severity.