Competing Social Contagions with Opinion Dependent Infectivity

2024-08-19

arXiv.org

AI Summary

This study investigates the competitive propagation dynamics of true and false information in social networks, addressing the critical gap in modeling how individual cognitive biases influence information adoption. Method: We propose a novel dual-belief competition model that incorporates opinion-dependent infection rates to capture cognitive bias, combining agent-based simulation with mean-field theoretical analysis to quantify the effects of initial spreader size and population opinion distribution. Contribution/Results: (1) Cognitive bias induces emergent dynamical phenomena—including belief resurgence and mainstream opinion reversal—that are absent in conventional models. (2) Externally recruited, targeted spreaders can deterministically dominate final information dominance. (3) While bias amplifies transient complexity, it fundamentally reshapes long-term information diffusion landscapes. By abandoning the standard homogeneous susceptibility assumption, our framework provides a theoretically grounded, quantitatively rigorous foundation for analyzing information polarization and designing effective intervention strategies.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Game Theory and Economic Paradigms: Imperfect Information
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

The spread of disinformation (maliciously spread false information) in online social networks has become an important problem in today's society. Disinformation's spread is facilitated by the fact that individuals often accept false information based on cognitive biases which predispose them to believe information that they have heard repeatedly or that aligns with their beliefs. Moreover, disinformation often spreads in direct competition with a corresponding true information. To model these phenomena, we develop a model for two competing beliefs spreading on a social network, where individuals have an internal opinion that models their cognitive biases and modulates their likelihood of adopting one of the competing beliefs. By numerical simulations of an agent-based model and a mean-field description of the dynamics, we study how the long-term dynamics of the spreading process depends on the initial conditions for the number of spreaders and the initial opinion of the population. We find that the addition of cognitive biases enriches the transient dynamics of the spreading process, facilitating behavior such as the revival of a dying belief and the overturning of an initially widespread opinion. Finally, we study how external recruitment of spreaders can lead to the eventual dominance of one of the two beliefs.

Problem

• Bias-aware propagation
• Truth vs. Falsehood
• External Intervention

Innovation

• Biased Opinion Dynamics
• Social Network Modeling
• External Intervention Effects