Detecting Depression Among Social Media Users Using Machine Learning

Depression is a serious and pervasive mental disorder that affects people for several reasons, such as occupational stress, academic pressure, personal matters, or other medical conditions. The clinical name of the disorder is major depressive disorder. Even today, depression is considered taboo. People avoid discussing it because they are afraid of being perceived as unstable or “psychotic.” This often leads to suffering in silence, sometimes to an extent where timely treatment becomes difficult or impossible. The proposed content-based model incorporates machine learning in the form of techniques like natural language processing and deep learning to analyze user-generated text, extract contextual cues, and identify linguistic features associated with depression. To keep in mind ethical considerations with respect to privacy and security, a labeled dataset will be used for training and testing the model on which the tool for early detection of subjects at risk for the development of depressive symptomswill be developed. Building on previous studies, this research further advances the new developing field of mental health informatics by taking advantage of machine learning and recent advances in technology for proactive action against mental illness within online environments.