Artificial Intelligence in Mental Health: Detecting Depression and Anxiety Using Social Media Data

Abstract

Mental health problems, including sadness and anxiety, have become important public health issues that affect more than 280 million people around the world. For treatment to work, it is important to diagnose problems early and start treatment right once. Unfortunately, standard therapeutic methods often fail because of underreporting, stigma, and limited access. As social media sites become more popular, user-generated content becomes a valuable source of real-time data for spotting early indicators of mental health problems. Artificial Intelligence (AI), notably machine learning and natural language processing (NLP) approaches, have shown a lot of promise in finding patterns in linguistic, behavioral, and multimodal indicators that are linked to psychological distress. This review looks at the present state of using AI to find sadness and anxiety using social media analysis. It goes into data sources, methods, feature engineering, model performance, ethical issues, and limitations. It also talks about important problems including algorithmic bias, privacy issues, and how to use AI systems in real-world mental health care. The article ends by talking about future research directions, such as creating models that can be understood, adding more culturally varied datasets, and hybrid human-AI diagnostic systems to help mental health practitioners and improve early intervention tactics.