Leveraging Social Media and Natural Language Processing for Early Detection of Depressive Disorders

Abstract

Depression is a prevalent mental health disorder impacting over 280 million people worldwide, according to recent World Health Organization (WHO) estimates. It poses a substantial burden on individuals and societies, emphasizing the need for early detection and timely intervention. Despite the availability of treatment options, many affected individuals do not seek professional help due to barriers such as stigma, lack of awareness, and insufficient access to mental health services. With the widespread adoption of social media, people increasingly share their thoughts, feelings, and daily experiences online, providing an abundant source of user-generated content. This information can be harnessed to detect early signs of depression.In recent years, advancements in Natural Language Processing (NLP) and Machine Learning (ML) have paved the way for innovative approaches to analyzing social media data for mental health insights. By processing text-based content from platforms such as Twitter, Facebook, and Reddit, NLP techniques can identify linguistic patterns.