(1) Background: Risk perception is a key factor in motivating people to comply with preventive behaviors during the COVID-19 pandemic. Appropriate risk perception is important to enhance beliefs and promote emergency management response to public health events. (2) Objective: This study developed a public risk perception measurement method for social media data to understand the dynamic characteristics of risk perception and emotional expression during public health emergencies. (3) Methods: Utilizing text-mining techniques and deep-learning algorithms, risk perception was calculated from two dimensions (dread and unknown) as well as the emotional expression characteristics of 185,025 posts from 10 January 2020 to 20 March 2020 on Sina Weibo. We also analyzed the characteristics of risk perception at different stages of the crisis life cycle. Furthermore, drawing on arousal theory, we constructed dynamic response relationships between emotion type (angry, fearful, sad, positive, and neutral) and risk perceptions by a vector autoregressive (VAR) model. (4) Results: The results revealed that the public expresses significantly more dread words than unknown words in shaping the risk perception process. As for the characteristics of evolution, public risk perception had been at a high level since the outbreak stage, and there was a sudden increase and a gradual decrease in the level of public risk perception. We also found that there is a significant response relationship between positive emotion, angry emotion, and risk perception. (5) Conclusion: This study provides a theoretical basis for more targeted epidemic crisis interventions. It points out the need for health communication strategy makers to consider the public’s risk perception and emotional expression characteristics during public health emergencies.

Although official departments attempt to intervene against misinformation, the personal field often conflicts with the goals of these departments. Thus, when rumours spread widely on social media, decision-makers often use a combination of rigid and soft control measures, such as blocking keywords, deleting misinformation, suspending accounts or refuting misinformation, to decrease the diffusion of misinformation. However, existing methods rarely consider the interplay of blocking and rebuttal measures, resulting in an unclear effect of the double intervention mechanism. To address these issues, we propose a novel misinformation diffusion model called SEIRI (susceptible, exposed, infective, removed, and infective) that considers the double intervention mechanism and secondary diffusion characteristics. We analyse the stability of the proposed model, obtain rumour-free and rumour-spread equilibriums, and calculate the basic reproduction number. Furthermore, we conduct numerical simulations to analyse the influence of key parameters through comparative experiments. Finally, we validate the effectiveness of the proposed approach by crawling a real-world data set of COVID-19-related misinformation tweets from Sina Weibo. Our comparison experiments with other similar works show that the SEIRI model provides superior performance in characterising the actual spread of misinformation. Our findings lead to several practical implications for public health policymaking.

When a major public health incident breaks out, in order to prevent the explosion of multiple types of public opinion, relevant government departments need to guide the online public opinion according to the needs and characteristics of different audiences in order to achieve reasonable regulation and control. In this process, gender differences among the participating public in areas such as comprehension ability often affect the effectiveness of government guidance. A proper understanding of these differences will enable the government to allocate resources on the basis of needs to save resources and achieve the same goals with half the effort. This paper takes the outbreak of the COVID-19 as an example to analyze the gender differences among users in terms of the overall volume of participation and specific participation behaviors from the dimension of time and geographical locations. A total of 735,271 comments posted by users in responding to tweets published by 144 official government accounts on Weibo during the COVID-19 outbreak were collected and analyzed with a combination of the methods of natural language processing and propensity score analysis. The results show that in comparison to male users, female users participated more, and their responses were more emotionally expressive. Female users tended to respond faster than male users by 30 minutes to an hour, which allowed female users to play a more important role in the process of government guidance of public opinion during major public health incidents. Therefore, this study further provides policy recommendations for the government to provide reasonable guidance of public opinion and give future direction.