Execution and assessment of agentic influence operations in simulated social networks

2026-05-27

AI Summary

This study systematically evaluates the efficacy and underlying mechanisms of AI-driven influence operations on social networks. By constructing a synthetic social network platform that integrates multi-agent simulation, natural language generation, and belief dynamics modeling, the work quantifies for the first time the impact of three core strategies—narrative dissemination, information amplification, and counter-messaging—on audience beliefs. The findings reveal that information amplification achieves the broadest reach, counter-messaging is most effective in shifting opinions, and narrative dissemination requires substantially greater adversarial investment to yield measurable effects. Furthermore, the research uncovers an intrinsic relationship between the behavioral footprints of influence actors and the resulting effectiveness of their campaigns.

Abstract

This article evaluates AI-enabled influence operations in synthetic social networks through controlled simulations of narrative release, amplification, and counter-messaging. We measure exposure and belief change in agentic audiences, showing that amplification maximizes reach, counter-messaging shifts opinions most, and narrative release requires larger attacker footprints.

Problem

• influence operations
• social networks
• AI agents
• belief change
• narrative dissemination

Innovation

• AI-enabled influence operations
• synthetic social networks
• agent-based simulation
• belief change
• counter-messaging