IDENTIFYING PSYCHOLOGICAL DISORDERS IN ONLINE PLATFORMS USING INTENSE PATTERNS - ANOREXIA AND DEPRESSIVE CASE STUDIES

Abstract

 Mental diseases that impair thoughts and actions impact a large number of individuals all over the globe. Since it may improve the likelihood of offering support for people before their sickness progresses, a precise diagnosis of these diseases is challenging but crucial. Monitoring how people show themselves online, including what and how they write, or maybe more significantly, what emotions they convey in their online leisure correspondences, is one technique to do this. In this study, we examine two computational models that aim to illustrate the presence and diversity of experiences reported by consumers of digital entertainment. We employed 2 consecutive public information stream for anorexia nervosa and depression, two serious mental diseases. The findings imply that important information about netizens who are depressed or anorexic can be highlighted due to the presence and variation of emotions recorded by the proposed representations. Additionally, combining the two representations can benefit the display because it matches the best detailed method for sorrow and is only 1% less entertaining for anorexia. Additionally, these representations offer the chance to enhance the results' comprehensibility.