Objectives The studies on the spread of major animal diseases and its evolution of public opinion are of great significance to the improvement of epidemic prevention and public opinion guidance. With the development of Web 2.0 technology and the popularity of smart phones, various forms of social media platforms become important channels for obtaining, sharing and discussing hot topics. A large number of texts with geographical location information are generated, which have provided a new way for the research of animal epidemic and other emergencies.

Methods Taking Sina microblog data during African swine fever(ASF) spread in our country from 2018 to 2019 as the case study, the objective of this work is to establish the spread spatiotemporal characteristics analysis and public opinion mining model. First, the Mann-Kendall mutation detection method is introduced to objectively divide the epidemic transmission cycle and investigate the spatial distribution characteristics of different stages. Then, the latent Dirichlet allocation theme clustering model is used to describe the evolution of public opinion topics among different ASF epidemic stages. Finally, the primary factors influencing public opinion attention are explored based on the geographical detector method.

Results The results show that the spread of ASF in China showed a trend of spreading from northeast to southwest, and experienced four stages: Incubation, outbreak, fluctuation and recession.

Conclusions At each stage, public opinion around outbreak itself and the specific theme is derived, and with the development of epidemic derivative subject is more abundant, popular sentiment also from at each stage. Public opinion around outbreak itself and the specific derivative topics, and derivative topics become more abundant with the development of epidemic, public sentiment also gradually changes from negative to positive. Regional awareness of ASF is strongly influenced by pork consumption and production, rather than by local education and urbanization levels.