Tracking mob dynamics in online social networks using epidemiology model based on mobility equations

In this paper, we develop an epidemiology mathematical model of social media mobs based on a mobility control strategy to adjust the rate of spread in response to social interactions. We tested our susceptible-quarantined-counter-infective-recovered (SQCIR) epidemiology model solution on a Twitter dataset related to the COVID-19 spread from April 2020 to June 2020. Real-world data with high temporal resolution will enable us to develop near-real-time predictive analytics for a campaign. Our analysis focused on key terms, such as "lockdown" and "quarantine," to track public sentiment and engagement trends during the pandemic. Primary qualitative analyses, such as the social media mob free equilibrium (MFE) point, endemic equilibrium point, and basic reproduction number, were calculated. Our analysis reveals that the stability analysis shows the social media MFE point is locally asymptotically stable if. 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.