Predicting Depression Levels Using Social Media Posts: A Comprehensive Survey

Depression, a pervasive mental health condition, often goes undetected due to stigma and limited diagnostic approaches. This survey paper examines the potential of social media as a tool for early detection of depression, leveraging the vast data repository of emotions, behaviours, and textual expressions. Through an exploration of various studies utilising machine learning, natural language processing, and data mining, this paper unveils the promising methodologies for identifying subtle markers indicative of depression. Studies, such as those analysing social media posts and interactions, reveal correlations between problematic social media usage and depression symptoms. However, ethical considerations concerning user consent, privacy, and data reliability persist as crucial challenges. The paper emphasises the necessity of navigating these ethical boundaries while harnessing the potential of social media for early depression detection.