State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China.
College of Information and Communication Engineering, Communication University of China, Beijing 100024, China.
Math Biosci Eng. 2022 Aug 9;19(11):11380-11398. doi: 10.3934/mbe.2022530.
A significant distinction between the COVID-19 pandemic and previous pandemics is the significant role of social media platforms in shaping public adherence to non-pharmaceutical interventions and vaccine acceptance. However, with the recurrence of the epidemic, the conflict between epidemic prevention and production recovery has become increasingly prominent on social media. To help design effective communication strategies to guide public opinion, we propose a susceptible-forwarding-immune pseudo-environment (SFI-PE) dynamic model for understanding the environment with direct and indirect propagation behaviors. Then, we introduce a system with external interventions for direct and indirect propagation behaviors, termed the macro-controlled SFI-PE (M-SFI-PE) model. Based on the numerical analyses that were performed using actual data from the Chinese Sina microblogging platform, the data fitting results prove our models' effectiveness. The research grasps the law of the new information propagation paradigm, and our work bridges the gap between reality and theory in information interventions.
新冠疫情与以往疫情的一个显著区别是社交媒体平台在塑造公众对非药物干预措施和疫苗接受程度方面的重要作用。然而，随着疫情的反复，社交媒体上的疫情防控与生产恢复之间的矛盾日益突出。为了帮助设计有效的沟通策略来引导舆论，我们提出了一个易感-传播-免疫伪环境（SFI-PE）动态模型，用于理解具有直接和间接传播行为的环境。然后，我们引入了一个具有直接和间接传播行为的外部干预系统，称为宏观控制 SFI-PE（M-SFI-PE）模型。基于来自中国新浪微博平台的实际数据进行的数值分析，数据拟合结果证明了我们模型的有效性。本研究抓住了新信息传播范式的规律，我们的工作弥合了信息干预中现实与理论之间的差距。
