Machine Learning-Based Classification of Mental Health Status on Social Media: A Case Study of Kunduz, Afghanistan

The proliferation of user-generated content results from social media's rapid development. An interdisciplinary field called computational cyber-psychology uses machine learning techniques to investigate fundamental psychological tendencies. Our study uses social media usage patterns to infer users' mental health status. The emergence of social media platforms in Afghanistan has profoundly affected the country's young people. This study aims to implement a predictive model using three machine-learning algorithms. The three selected algorithms are Naïve Bayes, Random Forest, and K-Nearest Neighbor (K-NN). The dataset contained in this study was collected through a questionnaire from students at Kunduz public and private universities in 2024. Data preprocessing is done before implementing the predictive models. The dataset is prepared carefully to ensure well-balanced samples in each category. The study reveals that Random Forest is the best classifier, with an accuracy of 77%. The study directly benefits the other researchers and policy or decision-makers in Afghanistan.