A New Method for Discovering Daily Depression from Tweets to Monitor Peoples Depression Status

Abstract

Many countries are actively involved in Mental Health Illness prevention programs as at present, this affects more than 300 million (>4%) people across the world, and this number is increasing every day. Predictions assume that Mental Health Illness will become the second leading cause for disease burden to stakeholders and rulers in the coming years. Identification of a mental health illness patient is complicated, as many do not agree that they have this stigma. Social Networks is one media that is involved in every ones' life to share/exhibit his emotions and feelings. More people share emotion-related tweets indicate that a predominant feature occurred on that day or in that location. We attempted to study the tweets related to depression and anti-depression and computed a new parameter, which indicates the depressive level of that day. While comparing with past data, this parameter will help the social scientists in the study of psychotherapy (afterburn) and ‘agitated depression’ levels to promote mental health and psychosocial interventions and sustainable development goals.