Detecting the Major Trends of Information Systems in the COVID-19 Research Landscape

As the volume and diversity of COVID-19 manuscripts grow, trend topic detection has become a more crucial issue to utilize information from pandemic-specific literature. Latent Dirichlet Allocation (LDA) and bibliometric analysis are common ways of detecting trend topics. In this study, a hybrid approach is suggested by combining both techniques as a novelty perspective to attain comprehensive information. The topics studied in the COVID-19 literature were outlined with the LDA analysis, and then the COVID-19 studies were examined specifically in the field of information systems (IS) with bibliometric analysis. As an outcome of LDA analysis, it has been determined that the topics studied on COVID-19 are concentrated under the categories of clinical studies, epidemiology and transmission of COVID-19, national and global policy responses to the COVID-19 pandemic, and the impacts of the COVID-19. Infodemiology in social media, computer-aided detection methods for diagnosis, information systems for contact tracing and health systems, distance learning solutions, data analytics for modeling and forecasting COVID-19, epidemiology, molecular docking of COVID-19 are primary topics of IS literature in COVID-19 era. This paper assists researchers in providing a comprehensive view of the compatibility of COVID-19 literature at a macro level and in the scope of IS and also offers suggestions for future work by IS researchers.