标题： 使用流行病学模型分析社交媒体网络中的群体动力学 显示英文标题

标题： Analyzing mob dynamics in social media networks using epidemiology model

摘要： 流行病学模型传统上用于研究疾病传播，可以通过将思想、情绪或行为视为“传染性”因素，通过用户网络传播，从而有效分析社交媒体上的群体行为。 在本研究中，我们引入了一个数学模型，通过分析2020年4月至2020年6月的Twitter活动来分析与新冠疫情传播相关的社会行为。 我们的分析集中在关键术语如“封锁”和“隔离”上，以追踪疫情期间公众情绪和参与趋势。 得出临界数$\Re_{0}$，并建立了稳态的稳定性。 进行了适用参数的数值模拟和敏感性分析。 结果表明，与积极情绪相比，推特上的负面情绪对新冠疫情传播的影响较小。 然而，负面情绪对新冠疫情传播的影响仍然显著。 此外，我们使用具有不同参数值的Caputo算子来研究社交媒体平台对新冠疫情传播的影响。 显示英文摘要

摘要： Epidemiological models, traditionally used to study disease spread, can effectively analyze mob behavior on social media by treating ideas, sentiments, or behaviors as ``contagions" that propagate through user networks. In this research, we introduced a mathematical model to analyze social behavior related to COVID-19 spread by examining Twitter activity from April 2020 to June 2020. Our analysis focused on key terms such as ``lockdown" and ``quarantine" to track public sentiment and engagement trends during the pandemic. The threshold number $\Re_{0}$ is derived, and the stability of the steady states is established. Numerical simulations and sensitivity analysis of applicable parameters are carried out. The results show that negative sentiment on Twitter has less influence on COVID-19 spread compared to positive sentiment. However, the effect of negative sentiment on the spread of COVID-19 remains remarkably strong. Moreover, we use the Caputo operator with different parameter values to study the impact of social media platforms on the transmission of COVID-19 diseases.