A SYSTEMATIC SURVEY OF IDENTIFYING DEPRESSION ON SOCIAL NETWORKING SITES

Abstract

Social Networking Sites (SNS) are being used for online communication more frequently for many years. People and groups openly discuss their opinions and share their experiences, feelings, and thoughts including their mental health. One of the most discussed and predominant mental health disorders today is Depression. Depression has become the leading cause of disability and premature mortality somewhat due to a lack of effective methods for early detection. However, the diagnosis rate of mental illness including depression has been improved in the last few years but still many cases remain undetected. Significant work can be done in academic research by getting text data from posts and comments of people on social networking sites. SNS can potentially provide inexpensive early detection of individuals who might require a specialist’s evaluation, based on their naturally occurring linguistic behavior. Symptoms regarding mental illnesses more specifically Major Depressive Disorder are observable on Social Network Sites and web forums. Automated methods are increasingly able to detect depression and other mental illnesses. Depressed users have been identified from normal users by patterns in their language and online activity. Many approaches of text analysis have been employed to identify and  predict depressed users through such websites. In this paper, a systematic review of the literature to predict Major Depressive Disorder (MDD) has been conducted. Large-scale monitoring of social media and automated detection methods could identify depressed or otherwise at-risk individuals, which may help to increase the well-being of an identity and complement existing screening procedures.