Opinion Polarization in LLM-Based Social Networks: Manipulation and Mitigation

2026-06-17

AI Summary

This study investigates whether large language model (LLM)-driven social networks exacerbate opinion polarization under malicious manipulation and proposes mitigation strategies. To this end, the authors develop an LLM-based simulation framework in which agents with diverse personas dynamically update their opinions through natural language interactions. This framework enables the first systematic analysis of polarization-inducing attacks and corresponding defense mechanisms, overcoming the oversimplified assumptions about interaction dynamics and content expression prevalent in traditional models. Experimental results demonstrate that even resource-constrained adversaries can significantly intensify polarization. While both reactive and proactive interventions partially mitigate these effects, they fail to fully restore the system to its original state, revealing an inherent vulnerability in LLM-mediated social networks.

Abstract

How vulnerable are online social networks to adversaries who seek to amplify opinion polarization by manipulating opinions, and how difficult is it to mitigate such manipulation? Existing studies have examined this question using mathematical models of opinion dynamics. While these models offer valuable theoretical insights, they rely on simplified assumptions about interactions, message content, and opinion updates, limiting the adversarial strategies they can capture and the applicability of their findings to real-world settings. Large language model (LLM)-based simulations provide a richer alternative: agents can be assigned diverse personas, communicate through natural language, and respond to persuasive or adversarial content in a context-dependent way. This enables the study of manipulation strategies that are difficult to represent using classical mathematical models. To the best of our knowledge, this study provides the first systematic analysis of polarization amplification and mitigation in an LLM-based simulated social network framework. In our framework, LLM agents with diverse personas interact over a social network by exchanging natural language posts and updating their opinions accordingly. We show that even an adversary with a limited manipulation budget can considerably increase polarization. We then study two classes of defense mechanisms: reactive mitigations, which assign specific users to actively counter manipulation, and proactive interventions, which increase resistance through general mechanisms not tied to particular users. Our results show that although these mechanisms reduce the impact of adversarial attacks, they generally do not restore the network to its baseline polarization state. These findings suggest that neither approach fully overcomes the vulnerability of the network, highlighting the potential risk of such attacks.

Problem

• opinion polarization
• adversarial manipulation
• social networks
• LLM-based simulation
• mitigation

Innovation

• LLM-based simulation
• opinion polarization
• adversarial manipulation
• natural language interaction
• mitigation mechanisms