Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study

In a fast-evolving public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi-steady-state phase of the old information.

This study aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users.

We developed and analyzed two classes of susceptible-forwarding-immune information propagation models with delay in transmission to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information were posted frequently by each opinion leader related to COVID-19 with high influence (data acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of a publishing strategy were analyzed.

The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation.

Our model can carry out an accurate numerical simulation of information at different release intervals and achieve a dynamic evaluation of information transmission by constructing an indicator system so as to provide theoretical support and strategic suggestions for government decision making. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management.