Three-Phase Methodology to Manage the COVID-19 Information for Classification of Mental Illness

Abstract

In the COVID-19 era, the use of social media platforms has significantly increased leading to misinformation being produced whose management is quite necessary for the domain experts, such as the Reddit social platform where people disseminate extensive information about their health issues using relevant posts and comments. The management of misinformation about COVID-19 impact on mental illness could be quite beneficial for the domain experts. In this regard, we proposed a two-step methodology which could aid domain experts to manage and group the posts and comments information with respect to COVID-19 impact on mental illness. First, we extract the information of well-known mental illnesses (such as depression, anxiety, OCD and PTSD) from the Raddit platform. Second, we leverage the capabilities of unsupervised learning algorithms and text categorisation approach to manage the information. We also proposed the evaluation model to assess the efficacy of the proposed method according to expert opinion. The experimental results indicate the efficacy of the proposed method. Moreover, we observed fuzzy c-means as an outperformed learner (with [Formula: see text]) as compared to K-means ([Formula: see text]) and Agglomerative ([Formula: see text]).