The Impact of Individual Behaviors and Governmental Guidance Measures on Pandemic-Triggered Public Sentiment Based on System Dynamics and Cross-Validation

Abstract

Negative online public sentiment generated by government mishandling of pandemics and other disasters can easily trigger widespread panic and distrust, causing great harm. It is important to understand the law of public sentiment dissemination and use it in a timely and appropriate way. Using the big data of online public sentiment during the COVID-19 period, this paper analyzes and establishes a cross-validation based public sentiment system dynamics model which can simulate the evolution processes of public sentiment under the effects of individual behaviors and governmental guidance measures. A concrete case of a violation of relevant regulations during COVID-19 epidemic that sparked public sentiment in China is introduced as a study sample to test the effectiveness of the proposed method. By running the model, the results show that an increase in government responsiveness contributes to the spread of positive social sentiment but also promotes negative sentiment. Positive individual behavior suppresses negative emotions while promoting the spread of positive emotions. Changes in the disaster context (epidemic) have an impact on the spread of sentiment, but the effect is mediocre.

Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic

“…However, identifying the pictures published by users could help analysts better understand the users' sentiment, as a result, the authors proposed a cross-media public sentiment analysis system that details the sentiment outcome by topic, region, microblog content, and user diffusion. Huang et al ( 4 ) proposed a framework that can be used to build and improve models of public epidemic sentiment based on theories such as system dynamics and cross-validation. A specific SD model was built to simulate the dissemination process of a public sentiment system by analyzing the mechanisms and influencing factors of online public sentiment dissemination.…”

Parallel simulation and optimization framework of supplies production processes for unconventional emergencies

“…According to the queuing network theory [26], the simulation model in this paper is a series-parallel queuing network system. The processing time of each Production Equipment (SF FE ) at each node obeys the normal distribution, and the formula is expressed as: f ðtÞ ¼ 1 ffi ffi ffiffi ð2s 2 Þ (μ represents the processing rate of PE, and σ represents the standard deviation of the processing time). On the basis of the above, we can use IF EOD-PP to represent the emergency demand orders entering the simulation production process, and the arrival rate is: l ¼ D t (D refers to the number of orders entering the production process in time t).…”

“…On the one hand, hospitals and other public places will consume large amounts of protective materials and equipment. On the other hand, the public will blindly purchase a large number of protective products due to information asymmetry and fear of unknown viruses [ 1 ]. Therefore, the surging demand makes the state of demand and supply in the conventional situation no longer balanced.…”

DES-Based Optimization of Hospital Consultation and Treatment Process under the COVID-19

“…The whole process of sorting out, refining and improving existing processes is called process optimisation (Ye, 2016). In the process of implementation and design, the process should be continuously improved to achieve the best results, mainly by reducing time, reducing or simplifying operational steps and making the best use of available personnel, funds and materials (Zhang, 2019;Xie et al, 2020;Huang et al, 2021). Currently, in order to cope with the problems faced in the new environment and the traditional intelligent management model, business process optimisation has become an inevitable choice in order to gain a foothold in the fierce market competition of the future (Xie et al, 2016).…”