Worldwide COVID-19 Topic Knowledge Graph Analysis From Social Media

Yuchen Zhang, Yifan Wang, Hao Fan, Jing Li, Jia Wu

Abstract: Current research on online public opinion regarding the coronavirus disease in 2019 (COVID-19) leverages keyword extraction, sentiment analysis, and topic modeling to analyze online public opinion. The multi-granularity features of online public opinions and semantic relations between the features, how-ever, remain less explored. Reliance on only topics or keywords for measuring public opinion is insufficient as topics are too broad and keywords too narrow. Analysis at an intermediate level, which most studies overlook, is crucial in gaining a clearer insight into public opinion. Additionally, exploring the semantic relationships between components of online public opinion can shed light on the logical connections between them and help understand how they interact in the dissemination of online public opinion, leading to a better understanding of its evolution mechanism. We conducted a public opinion analysis on Worldwide COVID-19 outbreaks via Topic Knowledge Graph. Specifically, we first use the Combined Topic Model to extract public opinion topics. Then multi-dimensions attributes of the topic such as [subject, predicate, object] triples, topic popularity, and topic emotion intensity are extracted. Subsequently, the semantic relations of different public opinion topics are calculated from the two levels of predicates co-occurrence and subject-object sharing. Finally, we applied the constructed framework to the public opinion information related to COVID-19 and analyzed the characteristics in the evolution of online public opinion.