Toward Systemic Immunization: Modeling Disinformation Propagation Dynamics With Intervention and Network-Driven Risk...

Area of Science:

• Computational Social Science
• Network Science
• Behavioral Economics

Background:

• Disinformation on social media threatens digital societies, causing cognitive distortion and public crises.
• Existing models fail to capture individual heterogeneity, complete rationality assumptions, or cross-scale risk coupling.

Purpose of the Study:

• To propose a novel disinformation propagation model integrating individual dynamics, network topology, and intervention analytics.
• To develop a theoretical framework for a disinformation immune system and provide quantifiable decision-making tools for digital governance.

Main Methods:

• Constructed a network propagation model incorporating risk perception, decision sensitivity, and cognitive inertia.
• Developed a dual-index system for identifying superspreaders based on risk exposure and vulnerability.
• Formulated an intervention model combining cognitive correction and propagation suppression, analyzing equilibrium and convergence.

Main Results:

• Proved equilibrium existence in the nonintervention model and derived conditions for unique convergence under interventions.
• Established the bounded intervention efficacy theorem and derived lower bounds on convergence time.
• Quantified the joint modulation of systemic risks by individual heterogeneity and intervention intensity.

Conclusions:

• The proposed model offers a comprehensive framework for understanding and mitigating disinformation propagation.
• Findings provide quantifiable tools for digital governance, enhancing resilience against systemic risks posed by fake news.