User depression and severity level prediction during COVID-19 epidemic from social network data

In recent times, depression becomes a major issue, which causes suicide, particularly among youngsters. During the Coronavirus disease (COVID-19) epidemic, many organizations suggested social distance and quarantine actions, which cause major attention to the mental health and depression of each individual. Most population express their emotions by using modern social media technologies like Twitter, Facebook, etc. By considering user tweets, a Long Short-Term Memory (LSTM)-based classifier was designed that learns rich attributes such as psychological, contextual, cognitive, person-level and distress-dependent n-gram attributes of each user for depression severity level prediction. But, it did not learn the social network structural properties of the most prominent communities, which influences the prediction outcomes. Hence in this article, a novel model is developed to predict the user’s depression severity levels by considering the social network structure of most prominent communities and influence measures. At first, it analyses the physical characteristics of the most prominent groups based on their balanced local and global power distribution. Then, the influential users and communities are identified along with the rich group of attributes. Moreover, those attributes are provided to the LSTM classifier for the user’s depression severity level prediction during the COVID-19 epidemic. Finally, the investigational outcomes exhibit that the presented model attains 93.53% accuracy and 0.4376 Root Mean Square Error (RMSE) contrasted with the conventional classifiers to estimate the user’s depression severity level.