Detecting Mental Health Conditions from Reddit Posts using Machine Learning

Social media platforms such as Reddit host a large volume of user-generated content that can reveal indicators of mental health conditions. This study presents a machine learning framework for the multi-class detection of mental health states, including Normal, Depression, Suicidal, Anxiety, Bipolar, Stress, and Personality Disorder, from Reddit posts. A dataset of relevant subreddits was collected and labeled in these seven categories. The text was cleaned, and features were extracted using TFIDF, LDA topics, sentiment scores, and basic linguistic cues. Multiple classifiers were trained, including logistic regression, linear SVM, random forest, Light Gradient Boosting Machine, and XGBoost, with SMOTE oversampling applied to address class imbalance. A stacked ensemble model combining the base classifier outputs achieved the best performance with an accuracy of 91%, macro-F1 of 0.92, and AUC of 0.93. These results show that interpretable, relatively lightweight machine learning models can accurately classify a range of mental health states from social media texts, supporting the development of scalable tools for the early detection and intervention of mental health issues.