Assessing the Dual Impact of the Social Media Platforms on Psychological Well-being: A Multiple-Option Descriptive-Predictive Framework

Abstract A comprehensive and recent exploration into the relationship between Social Media Platforms (SMP) usage and Social Media Disorders (SMD) is currently investigated as a topic of increasing importance given the surge in SMP use over the last two decades. The approach of analyzing data from 479 individuals across various SMP using clustering is particularly noteworthy for identifying the risk profile of the users and understanding the diverse impacts of SMP on mental health. In this paper, a multiple-option descriptive-predictive framework for assessing the impact of the SMP on the psychological well-being is proposed. This method effectively categorizes mental health states into distinct groups, each indicating different levels of need for professional intervention. Out of 5 clustering algorithms, K-prototypes proved to bring the best results with a silhouette score of 0.596, whereas for predicting clusters, Random Forest (RF) and eXtreme Gradient Boosting (XGB) outperformed K-Nearest Neighbors (KNN) and Support Vector Classifier (SVC), providing the highest accuracy and F1 score (0.993). Moreover, we analyze the connectedness between each SMP, anxiety and depression. Two distinct clusters emerged: Cluster 0 “Stable Professionals”, Cluster 1 “Vibrant Students”, and new instances are seamlessly predicted. While Youtube is the most popular platform among the respondents, Instagram shows a relatively higher correlation with both anxiety (0.256) and depression (0.186), indicating a stronger association with these disorders compared to other platforms.