Modeling Disinformation Spread in Social Networks: Phase Transitions and Mean-Field Analysis

The pervasive spread of disinformation across social media platforms has become a significant global challenge, disrupting democratic processes, undermining public trust and fueling societal polarization. Existing approaches often neglect the dynamic and structural mechanisms that drive the spread and adoption of false narratives. This paper leverages the well-established principles and methodologies of Statistical Mechanics and introduces a dynamic Mean-Field framework to model the evolution of disinformation within social networks. The framework introduces innovative elements, including heterogeneous coupling strengths to capture diverse social influences among network users, memory effects to account for cognitive inertia or belief re-evaluation and a three-state Potts model to represent polarization and neutrality in opinion dynamics. It employs the concept of effective fields to integrate external disinformation campaigns, facilitating a detailed analysis of critical thresholds and phase transitions. Monte Carlo simulations are performed to further illustrate the transient and equilibrium dynamics of belief adoption and rejection. Our findings provide actionable insights for the disinformation spread and offer a theoretical foundation for designing targeted interventions to mitigate its harmful effects on societies.