Topic Evolution Analysis of COVID-19 News Articles

Abstract

Abstract Recently, numerous media publishes various news on the latest developments every day due to the global spread of COVID-19. The news provides rich information about COVID-19 and includes a wide range of evolving topics. Our study is intended to develop a dynamic topic analysis system to monitor the evolution of the large-scale text data topics and assist with the social management and policymaking. The system expands the Dynamic Topic Model (DTM) with two modules: data sparsity computing and topic number selecting, which makes the experimental process more natural and generalizable. Data sparsity is designed to determine the length of single time slice. UCI, UMass and NPMI are introduced for choosing the optimal number of topics. This paper explores CBC news articles using DTM and captures the impact of COVID-19 on various aspects and the development of specific events. The experimental results demonstrate the effectiveness of our system for discovering and tracking the evolving topics. This system also plays an important role to improve the awareness of the public and serves as an analysis platform for government.