Social media information extraction and public opinion mining for African swine fever epidemic

The study 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 were generated, which have provided a new way for the research of animal epidemic and other emergencies. Taking Sina microblog data during African swine fever spread in our country from 2018 to 2019 as the case study, the objective of this work is to establish the spread spatio-temporal characteristics analysis and public opinion mining model. Firstly, the Mann-Kendall mutation detection method was 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 was used to describe the evolution of public opinion topics among different ASF epidemic stages. Finally, the primary factors influencing public opinion attention were explored based on the geographical detector method. The results show that the spread of African swine fever in China showed a trend of spreading from northeast to southwest, and experienced four stages: incubation, outbreak, fluctuation and recession. 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 became more abundant with the development of epidemic, public sentiment also gradually changed from negative to positive. Regional awareness of ASF is strongly influenced by pork consumption and production, rather than by local education and urbanization levels.