AI-based Mental Health Diagnosis and Prognosis of Youth during COVID-19

The COVID-19 pandemic's global lockdown and physical isolation have a negative impact on youth mental health. The rise of social media around the globe enables individuals to express, self-reflect, and connect with peers. As a result, we extrapolate mental health and well-being trends in three ways: social media anxiety disorder, anxious depression social media verbalization, and social anxiety. Based on linguistic clues and user posting behaviors, we developed a predictive algorithm to detect mental health disorders in online real-time Twitter messages using supervised learning algorithms. We use three classifiers; Random Forest, Nave Bayes, and AdaBoostM1.