Governing Emotional Contagion in Online Social Networks

This paper proposes a novel framework for governing the spread of negative emotions in online social networks (OSNs). The proposed SEIGR (Susceptible-Exposed-Infected-Guide-Recovered) model incorporates heterogeneous network structures and external interventions, such as guidance from governmental or expert sources, within a propagation dynamics-based approach. Optimal control theory is applied to derive strategies that minimize the spread of negative emotions under cost constraints. Numerical simulations on scale-free networks and validation with real-world social media data from the COVID-19 pandemic demonstrate the model's effectiveness in managing emotional contagion and its practical utility in guiding interventions within OSNs.