Temporal and spatial evolution of online public sentiment on emergencies

The transmission of online emergency information has become an active means of expressing public opinion and has vitally affected societal emergency response techniques. This paper analyzes interactions between three groups in time and space using a classic SIR (susceptible, infected, and recovered) epidemic model. Through social network theory and analog simulation analysis, we utilize data from China's Sina Weibo (a popular social media platform) to conduct empirical research on 101 major incidents in China that occurred between 2010 and 2017. We divide these emergencies into four types—natural disasters, accidents, public health events, and social security events—and conduct a simulation using three examples from each group. The results show that government control of public opinion is both cheaper and more effective when it occurs at the initial stages of an incident. By cooperating with the government, the media can facilitate emergency management. Finally, if netizens trust the government and the media, they are more likely to make cooperative decisions, maintain interest, and improve the management of online public sentiment.