You Can't Fool Us: Understanding the Resilience of LLM-driven Agent Communities to Misinformation
Chichen Lin, Yijie Jin, Kangbo Hu, Weijian Fan, Han Xiao, Yongbin Wang, Zhihui Ying, Zhanzhan Zhao

TL;DR
This study uses an LLM-based agent simulation to analyze how community traits like open-mindedness and political ideology influence resilience to misinformation, highlighting mechanisms and intervention effects.
Contribution
It introduces a synthetic community simulation framework to examine the joint impact of evidence-seeking traits and political ideology on misinformation response and correction.
Findings
Higher Actively Open-minded Thinking (AOT) enhances resistance and recovery from misinformation.
Moderate communities recover more reliably than polarized ones.
Interventions like persuasion and fact-checking improve post-peak correction effectiveness.
Abstract
Misinformation resilience is a dynamic community process: communities differ not only in whether they initially trust false claims, but also in how they recover through interaction, questioning, correction, and support withdrawal. We study this process with an LLM-based agent simulation that constructs synthetic communities along two theoretically motivated dimensions: Actively Open-minded Thinking (AOT), which captures evidence-seeking and willingness to revise beliefs, and Political Ideology (PI), which captures identity-based interpretation of contested claims. These two traits allow us to examine how evidence-oriented reasoning and ideological alignment jointly shape community responses to credible misinformation shocks. Across systematically varied AOT-PI communities, we find that higher AOT improves both resistance to misinformation uptake and recovery after trust peaks. PI shapes…
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