The surge in social networking’s popularity has led to a rise in social network mental disorders (SNMDs). These include Cyber-Relationship Addiction, Information Overload, and Net Compulsion. Early detection is crucial. We propose a machine learning approach, SNMDD, leveraging social network data to identify SNMD cases. Our innovative STM model enhances accuracy and scalability. Evaluation involving 3,126 users confirms SNMDD’s promise in identifying potential SNMDs.

• Machine Learning in Healthcare
• Mental Health Detection
• Social Network Disorders