Deep Learning for Mental Health Disorder Via Social Network Analysis

Mental health disorders are becoming very common, and depression and stress have grown into a significant issue in our culture. The majority of the population is currently affected by depression, a highly serious and severe mental ailment brought on by a variety of factors, including stress from jobs, studies, personal relationships, other illnesses, and other factors. It is also known as serious depression and is a key contributing factor in suicide, particularly among teenagers. Even though depression is a highly prevalent disorder, it is nevertheless frowned upon to discuss it publicly. People are hesitant to discuss this illness for fear that others would think they are crazy. This resistance can occasionally be highly detrimental to the patient, advancing his condition to the point where he cannot be restored to health. According to WHO data, depression remains the second most common factor contributing to the world’s disease burden. Social media platforms possess shown to be a fantastic medium for people to talk about themselves in the emergence of such problems. Social media accounts can therefore reveal a lot about a user’s emotional condition and mental health. In the proposed study, we employ deep learning approaches to use social media to detect depressed individuals and quantify their depression intensity. The objective-setting method divides the strategy into two parts: the first one depends on the content’s time and reviewing the posted content or tweet for analysis, and writing patterns. With the use of various word embedding approaches and suggested metadata features, the performance of deep learning algorithms that were trained using training data is assessed to have results. The predictive strategy is used to identify depression or additional mental illnesses early on. The goal is to assist individuals with this condition in recognizing depression’s early signs, which could be advantageous to both them and their families.