Publication

Abstract : Depression is considered to be one of the most severe mental health issues globally; in many cases, depression may lead to suicide. According to a recent report by the World Health Organization (WHO), depression is a common illness worldwide and approximately 280 million people in the world are depressed. Timely identification of depression would be helpful to avoid suicides and save the life of an individual. Due to the widespread adoption of social network applications, people often express their mental state and concerns on such platforms. The COVID-19 pandemic has been a catalyst to this situation where the mobility and physical social connections of individuals have been limited. This caused more and more people to express their mental health concerns with such platforms. This work attempts to detect signs of depression from unstructured social media posts using machine learning techniques. Advanced deep learning approaches such as transformers are used for classifying social media posts that will help in the early detection of any signs of depression in individuals. The experimental results show that machine learning approaches may be efficiently used for detecting depression from user-generated unstructured social media posts.