AI Contagion in Social Networks

2026-06-13

AI Summary

This study investigates how AI-generated content interacting with social network dynamics can induce systematic distortions in collective knowledge. The work proposes a novel dual-feedback mechanism—comprising an “AI contagion channel” and a “social distortion amplifier”—by modeling agents that disseminate information influenced by AI, while the AI itself is retrained on socially generated data already contaminated by its prior outputs. Leveraging dynamical systems theory, spectral radius analysis, and network science, the authors reduce the high-dimensional system to a tractable two-dimensional representation, rigorously characterizing conditions for system stability. They identify the minimal regulatory filtering threshold required to maintain stability and quantify how network topology modulates informational risk.

Abstract

We study how artificial intelligence (AI) interacts with social communication networks to shape the stability of collective knowledge. Agents exchange information through a network while receiving AI-generated content, and AI systems retrain on the aggregate social information they influence. This interaction generates two feedback forces: an AI contagion channel, through which distortions diffuse across the network, and an AI social distortion multiplier, through which retraining amplifies past errors. Despite the high dimensionality of the environment, we show that the long-run behavior of the system admits a two-dimensional representation whose spectral radius determines whether AI-mediated information systems are dynamically stable or unstable. We characterize a sharp regulatory frontier identifying the minimum filtering required for stability and show how network topology shapes systemic informational risk.

Problem

• AI contagion
• social networks
• collective knowledge
• information distortion
• systemic risk

Innovation

• AI contagion
• social distortion multiplier
• dynamic stability
• spectral radius
• network topology