Presently, the public’s perception of risk in terms of topical social issues is mainly measured quantitively using a psychological scale, but this approach is not accurate enough for everyday data. In this paper, we explored the ways in which public risk perception can be more accurately predicted in the era of big data. We obtained internal characteristics and external environment predictor variables through a literature review, and then built our prediction model using the machine learning of a BP neural network via three steps: the calculation of the node number of the implication level, a performance test of the BP neural network, and the computation of the weight of every input node. Taking the public risk perception of the Sino–US trade friction and the COVID-19 pandemic in China as research cases, we found that, according to our tests, the node number of the implication level was 15 in terms of the Sino–US trade friction and 14 in terms of the COVID-19 pandemic. Following this, machine learning was conducted, through which we found that the R2 of the BP neural network prediction model was 0.88651 and 0.87125, respectively, for the two cases, which accurately predicted the public’s risk perception of the data on a certain day, and simultaneously obtained the weight of every predictor variable in each case. In this paper, we provide comments and suggestions for building a model to predict the public’s perception of topical issues.

The communication of scientific topics can play a key role in the fight against misinformation and has become an important component of governments’ communication regarding COVID-19. This study reviewed the Chinese government’s COVID-19 information sources and identified the patterns of science communication models within them. A corpus of science-related content was collected and coded from 1521 news briefings announced by the Chinese government. An LDA (latent Dirichlet allocation) topic model, correlation analysis, and ANOVA were used to analyze the framing of the scientific topics and their social environmental characteristics. The major findings showed the following: (1) The frames in the Chinese government’s communication of scientific topics about COVID-19 had three purposes—to disseminate knowledge about prevention and control, epidemiological investigations, and the public’s personal health; to make the public understand scientific R&D in Chinese medicine, enterprises, vaccines, treatment options, and medical resources; and to involve citizens, communities, and enterprises in scientific decision making. (2) The frames were correlated with the public and media concerns. (3) The frames varied with the different levels of officials, different types of government agencies, different income regional governments, and different severity levels of the epidemic. (4) The topics concerning sustainability science were more correlated with public and media concern. In addition, we propose several suggestions for building sustainable communication approaches during the pandemic.