Large Language Model Driven Agents for Simulating Echo Chamber Formation

TL;DR

This paper introduces a novel LLM-based framework for simulating echo chamber formation on social media, capturing complex opinion dynamics and network changes more realistically than traditional rule-based models.

Contribution

It presents a new approach using large language models as generative agents to simulate social interactions, opinion updates, and network rewiring in echo chambers, validated against real Twitter data.

Findings

LLM-based simulation accurately models opinion clustering.

The framework captures structural and semantic aspects of echo chambers.

Benchmarking against Twitter data shows high realism.

Abstract

The rise of echo chambers on social media platforms has heightened concerns about polarization and the reinforcement of existing beliefs. Traditional approaches for simulating echo chamber formation have often relied on predefined rules and numerical simulations, which, while insightful, may lack the nuance needed to capture complex, real-world interactions. In this paper, we present a novel framework that leverages large language models (LLMs) as generative agents to simulate echo chamber dynamics within social networks. The novelty of our approach is that it incorporates both opinion updates and network rewiring behaviors driven by LLMs, allowing for a context-aware and semantically rich simulation of social interactions. Additionally, we utilize real-world Twitter (now X) data to benchmark the LLM-based simulation against actual social media behaviors, providing insights into the accuracy and realism of the generated opinion trends. Our results demonstrate the efficacy of LLMs in modeling echo chamber formation, capturing both structural and semantic dimensions of opinion clustering. %This work contributes to a deeper understanding of social influence dynamics and offers a new tool for studying polarization in online communities.