Propagation Patterns of Situational Information on Social Media During COVID-19: A Case Study of Weibo

This study employs Latent Dirichlet Allocation (LDA) for topic modeling and Social Network Analysis (SNA) to investigate how situational information propagated on Weibo during the COVID-19 pandemic. The study demonstrates how important pandemic-related information was shared and distributed among users by examining user interactions and information propagation patterns. The findings highlight how social media plays a part in the dissemination of information during emergencies, highlighting important influencers and the kinds of content that were popular. Insights into handling situational information on social media during public health emergencies are offered by this study.