This research introduces a novel framework using large language models (LLMs) as generative agents to simulate how echo chambers form in social networks, offering deeper insights than traditional rule-based approaches.

• Creates realistic simulations of how social media users interact and form polarized groups
• Demonstrates how LLMs can model complex social behaviors with nuanced human-like interactions
• Provides a testbed for studying intervention strategies against harmful polarization
• Offers security implications for combating misinformation spread and social fragmentation

This work matters for security professionals by revealing mechanisms behind echo chamber formation that contribute to radicalization and information manipulation. The approach enables testing countermeasures against dangerous polarization without real-world experimentation.

Large Language Model Driven Agents for Simulating Echo Chamber Formation