Analyzing and Predicting the Social Media Addiction Using Machine Learning

Abstract

The new reality of our modern life is social media. Modern science has given people a better civilization, increased the standard of living, but has taken away all the emotions of life. Social media has become an essential part of our modern lives. It significantly affects how people communicate, work, and interact with each other. Social media offers many benefits, such as increased connectivity and information sharing, but excessive use has led to growing concerns about addiction, which can negatively impact on mental health, productivity, and relationships. Our goal is to predict social media addiction and evaluate its impact on professional and personal life using machine learning algorithm. We used six machine learning classifiers like Random Forest, Extra Tree Classifier, Linear Discriminant Analysis (LDA), Gaussian Naive Bayes, and ensemble approaches including Stacking and AdaBoost to analyze a dataset of 522 samples that collected via structured questionnaires. Which included data from students and employed people. Outperforming the other models, Stacking Ensemble model achieved the highest accuracy of 91.71% in classifying social media addiction behaviors. This study not only provides valuable insights into identifying addiction behaviors, but it also proposes a practical solution in the form of a mobile application based on a superior predictive model. The goal of this application is to help people increase their consciousness and encourage people to adopt good social media practices.