School of Economics, Management and Law at the University of South China, Hengyang 421001, China.
Information Sciences and Technology at The Pennsylvania State University, State College, PA 16802, USA.
Int J Environ Res Public Health. 2021 Apr 16;18(8):4245. doi: 10.3390/ijerph18084245.
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.
政府对疫情和其他灾害处理不当而产生的负面网络舆情，容易引发广泛的恐慌和不信任，造成极大的危害。了解舆情传播规律，并及时、恰当地加以运用非常重要。本文利用 COVID-19 期间的网络舆情大数据，分析建立了基于交叉验证的舆情系统动力学模型，该模型可以模拟个体行为和政府引导措施对舆情演变过程的影响。引入中国 COVID-19 疫情期间一起违反相关规定的舆情事件作为研究样本，测试所提方法的有效性。通过模型运行，结果表明，政府响应速度的加快有助于积极社会情绪的传播，但也会促进消极情绪的传播。个体的积极行为抑制了负面情绪，同时促进了积极情绪的传播。灾害情境（疫情）的变化会对情绪传播产生影响，但效果一般。
