Tracking mob Dynamics in online social networks Using epidemiology model based on Mobility Equations

Nowadays, social media is the main tool in our new lives. The outbreak news and all related obtained from social media, and mob events affect the of spread these news fast. Recently, epidemiological models to study disease spread and analyze the behavior of mob groups by dealing with "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. The main feature of this model is the integration of mobility dynamics that be derived from the above real data, to adjust the rate of outbreak based on the response of social interactions. Consider mobility as a parameter of time-varying, and fluctuations in the rate of contact that is driven by factors like personal behavior or external affecting such as "lockdown" and "quarantine" etc., to track public sentiment and engagement trends during the pandemic. The threshold number is derived, and the existence of bifurcation and the stability of the steady states are established. Numerical simulations and sensitivity analysis of relevant parameters are also carried out.