Vol. 1, Issue 1, Part A (2024)

The early detection of mental health conditions such as depression, anxiety, and suicidal ideation is crucial for timely intervention and improved outcomes. Traditional diagnostic methods often rely on self-reporting and clinical interviews, which may not capture emerging symptoms in real time. With the widespread use of social media platforms, users frequently express emotional states and behavioral patterns online, creating a rich source of data for mental health analysis. This study proposes a data-driven approach to detect early indicators of mental health issues by mining user-generated content from platforms such as Reddit and Twitter. Using a combination of natural language processing (NLP), behavioral analysis, and machine learning models, including transformer-based BERT classifiers, the system analyzes linguistic features (e.g., pronoun use, sentiment, emotion words) and temporal patterns (e.g., posting time, frequency). The methodology includes data preprocessing, feature extraction, model training, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. Results show that BERT achieved the highest accuracy (91.2%) and recall (92.5%) across all conditions, outperforming traditional models such as SVM and logistic regression. The study also discusses ethical considerations including user privacy, data representativeness, and the need for human oversight. These findings demonstrate that social media mining, when applied responsibly, can serve as an effective early-warning system for mental health surveillance and support public health strategies.