# Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks

arXiv: https://arxiv.org/abs/2409.19338 · HTML: https://mlsanity.com/paper/2409.19338/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023)
- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [Can Large Language Model Agents Simulate Human Trust Behavior?](https://mlsanity.com/paper/2402.04559/) (2024)
- [From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News](https://mlsanity.com/paper/2403.09498/) (2024)
- [Lyfe Agents: Generative agents for low-cost real-time social interactions](https://mlsanity.com/paper/2310.02172/) (2023)
- [A Multi-Agent Conversational Recommender System](https://mlsanity.com/paper/2402.01135/) (2024)
- [Quantifying the Impact of Large Language Models on Collective Opinion Dynamics](https://mlsanity.com/paper/2308.03313/) (2023)
- [Me, My Echo Chamber, and I: Introspection on Social Media Polarization](https://mlsanity.com/paper/1803.01731/) (2018)
- [The Effect of People Recommenders on Echo Chambers and Polarization](https://mlsanity.com/paper/2112.00626/) (2021)
- [Out of the Echo Chamber: Detecting Countering Debate Speeches](https://mlsanity.com/paper/2005.01157/) (2020)
- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)

## Built on by (most cited first, 30 of 33)

- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.10708/) (2025)
- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [Characterizing LLM-driven Social Network: The Chirper.ai Case](https://mlsanity.com/paper/2504.10286/) (2025)
- [CoBRA: Programming Cognitive Bias in Social Agents Using Classic Social Science Experiments](https://mlsanity.com/paper/2509.13588/) (2025)
- [Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits](https://mlsanity.com/paper/2605.18890/) (2026)
- [Large Language Model Driven Agents for Simulating Echo Chamber Formation](https://mlsanity.com/paper/2502.18138/) (2025)
- [A Review of Incorporating Psychological Theories in LLMs](https://mlsanity.com/paper/2505.00003/) (2025)
- [Revealing Political Bias in LLMs through Structured Multi-Agent Debate](https://mlsanity.com/paper/2506.11825/) (2025)
- [Competing LLM Agents in a Non-Cooperative Game of Opinion Polarisation](https://mlsanity.com/paper/2502.11649/) (2025)
- [DynamiX: Large-Scale Dynamic Social Network Simulator](https://mlsanity.com/paper/2507.19929/) (2025)
- [A Natural Language Agentic Approach to Study Affective Polarization](https://mlsanity.com/paper/2603.02711/) (2026)
- [Agentic Microphysics: A Manifesto for Generative AI Safety](https://mlsanity.com/paper/2604.15236/) (2026)
- [MTOS: A LLM-Driven Multi-topic Opinion Simulation Framework for Exploring Echo Chamber Dynamics](https://mlsanity.com/paper/2510.12423/) (2025)
- [ElecTwit: A Framework for Studying Persuasion in Multi-Agent Social Systems](https://mlsanity.com/paper/2601.00994/) (2026)
- [Interpreting Emergent Extreme Events in Multi-Agent Systems](https://mlsanity.com/paper/2601.20538/) (2026)
- [Multilingual Large Language Models do not comprehend all natural languages to equal degrees](https://mlsanity.com/paper/2602.20065/) (2026)
- [Dynamics and Convergences for Markov Coevolutionary Opinion Formation Games in Dynamic Social Networks](https://mlsanity.com/paper/2607.05580/) (2026)
- [From Who They Are to How They Act: Behavioral Traits in Generative Agent-Based Models of Social Media](https://mlsanity.com/paper/2601.15114/) (2026)
- [To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs](https://mlsanity.com/paper/2604.06552/) (2026)
- [Cross-Cultural Simulation of Citizen Emotional Responses to Bureaucratic Red Tape Using LLM Agents](https://mlsanity.com/paper/2604.12545/) (2026)
- [HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation](https://mlsanity.com/paper/2605.23043/) (2026)
- [Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?](https://mlsanity.com/paper/2605.25929/) (2026)
- [Social Simulations: from Agent-Based Modeling to Digital Twins](https://mlsanity.com/paper/2607.13693/) (2026)
- [Eco3S: Complex Socio-Economic System Simulation via Agent-Based Models](https://mlsanity.com/paper/2607.26588/) (2026)
- [LLMs struggle to simulate human belief updates in controlled environments](https://mlsanity.com/paper/2607.28347/) (2026)
- [GraphWake: Group Polarization via Memory-Mediated Polarization Cascade in LLM-Agent Communities](https://mlsanity.com/paper/2608.17665/) (2026)
- [LLMs for Social Network Modeling: From Network Generation to Dynamic Processes](https://mlsanity.com/paper/2609.08049/) (2026)
- [GTA: Graph Theory Agent and Benchmark for Algorithmic Graph Reasoning with LLMs](https://mlsanity.com/paper/2609.12265/) (2026)
- [Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric](https://mlsanity.com/paper/2609.18306/) (2026)

## Abstract (arXiv)

The impact of social media on critical issues such as echo chambers needs to be addressed, as these phenomena can have disruptive consequences for our society. Traditional research often oversimplifies emotional tendencies and opinion evolution into numbers and formulas, neglecting that news and communication are conveyed through text, which limits these approaches. Hence, in this work, we propose an LLM-based simulation for the social opinion network to evaluate and counter polarization phenomena. We first construct three typical network structures to simulate different characteristics of social interactions. Then, agents interact based on recommendation algorithms and update their strategies through reasoning and analysis. By comparing these interactions with the classic Bounded Confidence Model (BCM), the Friedkin Johnsen (FJ) model, and using echo chamber-related indices, we demonstrate the effectiveness of our framework in simulating opinion dynamics and reproducing phenomena such as opinion polarization and echo chambers. We propose two mitigation methods, active and passive nudges, that can help reduce echo chambers, specifically within language-based simulations. We hope our work will offer valuable insights and guidance for social polarization mitigation.