Anticipating Depression Based on Online Social Media Behaviour

Abstract

Mental disorders are major concerns in societies all over the world, and in spite of the improved diagnosis rates of such disorders in recent years, many cases still go undetected. The popularity of online social media websites has resulted in new opportunities for innovative methods of detecting such mental disorders. In this paper, we present our research towards developing a cutting-edge automatic screening assistant based on social media textual posts for detecting depression. Specifically, we envision an automatic prognosis tool that can anticipate when an individual is developing depression, thus offering low-cost unobtrusive mechanisms for large-scale early screening. Our experimental results on a real-world dataset reveals evidence that developing such systems is viable and can produce promising results. Moreover, we show the results of a case study on real users revealing signs that a person is vulnerable to depression.