Users have various social influences due to their behavior characteristics in the Chinese Sina-Microblog. In order to quantify the impact of different user influences on the spread of public opinion such as public health emergencies, we establish a user-influence susceptible-forwarding-immune (UI-SFI) dynamic information propagation model, which is based on the forwarded quantities data of a real message about COVID-19 vaccine. By the method of binary tree classification, we use the median of followings, followers, and Weibo posts as the boundary to divide users into eight types who have different abilities for the propagation scope of the message. Our Model-based analyses show that the numerical results of the proposed eight types of average exposure rates are in accord with the actual situation. Describing important factors that affect the spread of information, our sensitivity analyses reveal two key types of users and support the strategies for regulating the development of public opinion.
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