ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION OF MENTAL HEALTH DISORDERS USING SOCIAL MEDIA DATA

Mental health conditions can be considered one of the most serious social disasters of the twenty-first century. “World Health Organization” (WHO) states that a global population of over one billion is living with some mental health problem, that over half a billion suffer depression and other disorders of anxiety and that each year, suicide kills about 727,000 with more than 580 million people affected. Timely intervention and early detection is desperately wanting especially in low and middle-income countries where more than three quarters of victims go untreated. The growth of social networks such as Twitter/X, Reddit, and Facebook produces large amounts of user-generated data that can record current emotional states, behavioural tendencies, and linguistic indicators and can serve as an unprecedented source of non-invasive data to monitor mental health. The paper is a systematic review of the use of artificial intelligence (AI)-based tools in the early prediction of mental health issues, such as depression, anxiety, bipolar disorder, and suicidal thoughts, with the help of social media data. The review summarizes more recent “natural language processing” (NLP), deep learning systems, including BERT, RoBERTa, and Bidirectional LSTM networks, multimodal fusion models, and “Explainable AI” (XAI) models related to improving clinical interpretability. Empirical results suggest state of the art transformer designs can do so with a depression detection accuracy of over 91, a suicidal ideation detection rate of up to 94.29 and the AI systems are able to detect other crisis telltales on average 7.2 days before professional clinicians. Data privacy, cross-cultural generalizability, and the Ethical aspects of autonomous mental health screening are highlighted as key issues of autonomous systems in healthcare. This review offers a guide on how AI-driven social media analytics can be responsibly integrated into the proactive mental health care systems.