Detection of Mentally Distressed Social Media Profiles Using Machine Learning Techniques

Abstract

Abstract In recent days, due to multiple reasons like the nuclear family, peer pressure for fake prestige, impatience attitude, and mental stress has become a common trait in every person. With advancements in technology like the internet and online presence, it has become a routine to be active online. Some sections of people vent out their emotions online as they have no support system in real life. It has been detected, as seen in some instances; those suicidal tendencies ranging from mild to extreme could be from a person’s online profile activity. However, it is a complex combination of multiple factors that must be comprehensively calculated lest it predicts a wrong result for an innocent person marking him as suicidal. In our current work, we use a specific method that includes all critical criteria that could be exhibited by a suicidal person by using Natural Language Processing (NLP) techniques. These textual features are passed through a robust Machine Learning framework for detecting an abrupt change in input data. Our method predicts efficiently a genuine, mentally disturbed profile from a typical profile.