A Systematic Review on Strategies of Depression Detection from Social Media

Abstract

Depression is considered as one of the severe global challenges of our current society. Millions of people suffer from depression each day. Beside feeling sad and lose of interests, depressed person may face various heath complexities or may commit suicide. Therefore, depression is said to be a “Global Burden”. Traditionally, psychiatrists conduct face to face interviews with a patient referring to depression criteria. Unfortunately, many patients feel shy to consult doctor which deteriorates their mental health even more. Additionally, everyday large number of people are sharing their thoughts and feelings on various social media platforms. Since people are spending huge amount of time online, researchers are actively linking online system with depression detection to understand the mental health of the individuals or even the entire population. So far, various efforts have been made to detect depression in users through their posts, tweets and messages on different social media platforms which generated successful outcomes. This paper reviews recent theories which were developed to detect depression from different social media. This survey aims in providing information about current depression detection methods, highlighting their core features, data source, findings and certain drawbacks, promoting further research in the field of depression detection and prevention.