Abstract

Depression, a leading cause of global disability and a significant risk factor for suicide, remains untreated in many due to various barriers. Recognizing the impact of depression on language patterns, this paper explores the innovative use of machine learning to analyze social media communications for early signs of depression. By harnessing advanced models to sift through digital interactions, this paper aims to identify linguistic indicators of depressive states, offering a novel approach to early detection. This study not only underscores the potential of social platforms as valuable data sources for mental health monitoring but also contributes to the development of proactive support systems, leveraging technology to bridge gaps in mental health care.