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The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies.

This study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space.

The findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages.

This study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.

Customer referral programs (CRPs) are popular; however, they often generate low referral rates. The authors propose that certain CRP referral tasks may hinder consumers’ referral likelihood. This study aims to explore the effects of referral tasks (communication content and approach) on customers’ referral likelihood on social platforms and the role of self-construal.

This study establishes a theoretical model based on online social platforms and conducts three scenario-based experiments. The authors obtain data from consumers on Sojump platform and test the hypotheses using analysis of variance (ANOVA) analysis and mediation analysis in SPSS. The valid sample sizes for these three experiments are 288, 203 and 214, respectively.

Three experimental studies indicate that communication content and approach have a significant effect on referral likelihood. Furthermore, the effect of communication content on referral likelihood depends on the communication approach. Self-construal plays a moderating role in the effect of communication content and approach on perceived social costs.

CRPs typically involve tasks and rewards; consumers are asked to complete a referral task and then receive a reward. Both tasks and rewards can affect an individual’s willingness to participate; however, existing studies on CRP focus primarily on the reward component. To the best of the authors’ knowledge, this is the first study to systematically investigate the role of referral tasks (communication content and approach) in CRPs. The authors extend the related research by examining the impact of referral tasks on consumers’ willingness to recommend. In addition, this study introduces self-construal into CRPs research.

The purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.

Two methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.

The causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.

The obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.

This paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.