The all-round and multilevel responses to the epidemic, with professional medical institutions, the governments and the public as the main agents of response and the media as the bridge of communication, are key to developing the public health emergency management system with regard to emerging infectious diseases and diseases with unknown etiology in the information age. This study creates an analysis framework concerning the five dimensions of information-the epidemic itself and the medical, governmental, public and media responses-and analyzes the evolution, interaction and trends of five dimensions using big data within the period of observation For the four dimensions other than the media response, the level of information related to the epidemic and the medical response is relatively high, while the level of response by medical institutions and the governments are similar, and both are higher than the public response. The media coverage of the epidemic remains at a high level of information. In relation to such diseases, the government should take the role of big data analytics seriously, lead a multi-agent social collaboration network, and further strengthen the 'One Planning Plus Three Systems' framework related to emergency management in China.

The COVID-19 pandemic has produced a far-reaching influence on higher education and the teaching activities of teachers in Chinese universities. The intentions of teachers in universities for using the micro-lecture, one of the educational informationization products, and the influencing factors of the intentions for using micro-lectures, are changing in the post COVID-19 era. This paper, based on the Technical Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), constructed the research hypotheses for the influence factors of micro-lecture usage intentions of teachers in universities in the post COVID-19 era, and made corresponding verifications through the Structural Equation Model (SEM). As shown by the results therefrom: (1) the micro-lecture usage experience before the outbreak of the COVID-19 pandemic significantly affected the usage intentions of teachers in universities; (2) the perceived usefulness influenced the usage intention directly, but the perceived ease of use did not directly produce influence; (3) policy impact had no significant influence on the perceived usefulness and the perceived ease of use of university teachers for micro-lecture use; (4) social relations and personal innovativeness have significant impacts on perceived usefulness, teaching objectives and micro-lecture characteristics have significant impacts on the perceived ease of use. In this paper, suggestions and opinions on popularizing micro-lecture usage in the post COVID-19 era were put forward on the basis of research conclusions therein.

The satisfaction of highly educated citizens with community services for COVID-19 represents the attitude of the middle class and plays an important role in both the social and political stability of a country. The aim of this paper was to determine which factors influence public satisfaction with COVID-19 services in a highly educated community. Through a literature review and using the American Customer Satisfaction Index (ACSI) model, this paper constructed a public satisfaction model of community services for COVID-19 and proposed relevant research hypotheses. A community with many highly educated residents in Beijing was selected as the case study, where 450 official questionnaires were distributed based on the age ratio of residents, with 372 valid questionnaires being collected from May 2021 to July 2021. The study results obtained by a structural equation model (SEM) show that: (1) public satisfaction is significantly and positively influenced by quality perception (0.305 **), public demand (0.295 **), and service maturity (0.465 ***); (2) public satisfaction has a significantly positive effect on service image (0.346 ***) and public trust (0.232 **), and service image significantly affects public trust (0.140 *); (3) service maturity is positively influenced by public demand (0.460 ***) and quality perception (0.323 *); and (4) public demand is positively influenced by quality perception (0.693 ***) (* p < 0.05; ** p < 0.01; *** p < 0.00). The conclusions of the study can provide suggestions and recommendations to improve the satisfaction of highly educated residents with community healthcare services during the COVID-19 pandemic.

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.