COVID-19 information propagation dynamics in the Chinese Sina-microblog.

The outbreak of a novel coronavirus (COVID-19) generated an outbreak of public opinions in the Chinese Sina-microblog. To help in designing effective communication strategies during a major public health emergency, we propose a multiple-information susceptible-discussing-immune (M-SDI) model in order to understand the patterns of key information propagation on social networks. We develop the M-SDI model, based on the public discussion quantity and take into account of the behavior that users may re-enter another related topic or Weibo after discussing one. Data fitting using the real data of COVID-19 public opinion obtained from Chinese Sina-microblog can parameterize the model to make accurate prediction of the public opinion trend until the next major news item occurs. The reproduction ratio has fallen from 1.7769 and maintained around 0.97, which reflects the peak of public opinion has passed but it will continue for a period of time.

Nearcasting forwarding behaviors and information propagation in Chinese Sina-Microblog.

As the largest social media in China, the Sina-Microblog plays an important role in public opinion dissemination. Despite intensive efforts in understanding the information propagation dynamics, the use of a simple outbreak model to generate summative indices that can be used to characterize the time series of a single Weibo event has not been attempted. This work fills this gap, and illustrates the potential of using a simple outbreak model in conjunction with the historical data about the cumulative forwarding users for nearcasting the propagation trend.