Extracting information from social media platform for early detection of depression among individuals

Abstract

In our world, 3.8% of the total population is suffering from depression and it is the fourth major cause of death in 15-29 year olds. It is estimated that more than 75% of people suffering from it in low and middle income countries receive no treatment. Also, in these countries, so many people live with such conditions without even recognizing it because of the lack of proper diagnosis and mental health facilities. However, a huge chunk of the population is connected and active on different social media platforms. Detection of depression from social media activities can help in recognizing the problems in an individual level and in a public health level to know its prevalence in different demographics. The early prediction of such can help us to work on the problem before the onset. In our work, we propose to use state of the art machine learning and deep learning models to provide an efficient early detection model for diagnosis of such. We hope that it would help individuals and relevant authorities to find out the illness and its severity for the betterment of global and regional mental health.