Predicting Mental Health Issues Using Social Media Data: A Machine Learning Approach

Mental health care has become an equal partner to physical well-being in recent years. The concern about mental health increases as the use of social media speeds up quite rapidly. Although such tools allow communication and information sharing, heavy usage of social media has resulted in several problems, among them anxiety, depression, and low self-esteem. This has motivated the behind the curtain mechanisms that use machine learning-based applications to produce effective solutions that concentrate on the early prediction of the degree and type of mental disorders and their possible treatment results. Machine learning in the healthcare system is viewed as one of the most successful methods because it deals with a lot of information smoothly and makes accurate predictions. This paper examines how ML can be used to predict early mental health problems using wide-ranging demographic and technology usage data. ML models that include the Logistic Regression, Decision Trees, K-Nearest Neighbors, and Random Forests, were tested. K-Nearest Neighbors realized the highest accuracy of 75.80%. This gives evidence of how high ML can stand in supplementing intervention for early mental health. These results shed light on the possibility of data-driven interventions in augmenting care for mental health.