# Simulating Rumor Spreading in Social Networks using LLM Agents

arXiv: https://arxiv.org/abs/2502.01450 · HTML: https://mlsanity.com/paper/2502.01450/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 6 of 6)

- [More Agents Is All You Need](https://mlsanity.com/paper/2402.05120/) (2024)
- [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023)
- [Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?](https://mlsanity.com/paper/2309.15943/) (2023)
- [Can Large Language Models Detect Rumors on Social Media?](https://mlsanity.com/paper/2402.03916/) (2024)
- [Rumor Detection and Classification for Twitter Data](https://mlsanity.com/paper/1912.08926/) (2019)
- [Why Rumors Spread Fast in Social Networks, and How to Stop It](https://mlsanity.com/paper/2305.08558/) (2023)

## Built on by (most cited first, 16 of 16)

- [Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities](https://mlsanity.com/paper/2506.18019/) (2025)
- [Population-Aligned Persona Generation for LLM-based Social Simulation](https://mlsanity.com/paper/2509.10127/) (2025)
- [The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies](https://mlsanity.com/paper/2509.18052/) (2025)
- [Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems](https://mlsanity.com/paper/2605.07069/) (2026)
- [Fast LLM Post-training via Decoupled and Fastest-of-N Speculation](https://mlsanity.com/paper/2511.16193/) (2025)
- [Agent-based simulation of online social networks and disinformation](https://mlsanity.com/paper/2512.22082/) (2025)
- [LLM Agents as Social Scientists: A Human-AI Collaborative Platform for Social Science Automation](https://mlsanity.com/paper/2604.01520/) (2026)
- [Simulating Misinformation Vulnerabilities With Agent Personas](https://mlsanity.com/paper/2511.04697/) (2025)
- [Agent-Based Simulations of Online Political Discussions: A Case Study on Elections in Germany](https://mlsanity.com/paper/2503.24199/) (2025)
- [Inject, Fork, Compare: Defining an Interaction Vocabulary for Multi-Agent Simulation Platforms](https://mlsanity.com/paper/2509.13712/) (2025)
- [MTOS: A LLM-Driven Multi-topic Opinion Simulation Framework for Exploring Echo Chamber Dynamics](https://mlsanity.com/paper/2510.12423/) (2025)
- [Public Discourse Sandbox: Facilitating Human and AI Digital Communication Research](https://mlsanity.com/paper/2505.21604/) (2025)
- [Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection](https://mlsanity.com/paper/2601.02750/) (2026)
- [Can LLMs Emulate Human Belief Dynamics?](https://mlsanity.com/paper/2605.18781/) (2026)
- [Should LLM Agents Decide in Social Simulations? Comparing Finite-State and LLM-Based Decision Policies](https://mlsanity.com/paper/2606.12369/) (2026)
- [LLMs for Social Network Modeling: From Network Generation to Dynamic Processes](https://mlsanity.com/paper/2609.08049/) (2026)

## Abstract (arXiv)

With the rise of social media, misinformation has become increasingly prevalent, fueled largely by the spread of rumors. This study explores the use of Large Language Model (LLM) agents within a novel framework to simulate and analyze the dynamics of rumor propagation across social networks. To this end, we design a variety of LLM-based agent types and construct four distinct network structures to conduct these simulations. Our framework assesses the effectiveness of different network constructions and agent behaviors in influencing the spread of rumors. Our results demonstrate that the framework can simulate rumor spreading across more than one hundred agents in various networks with thousands of edges. The evaluations indicate that network structure, personas, and spreading schemes can significantly influence rumor dissemination, ranging from no spread to affecting 83% of agents in iterations, thereby offering a realistic simulation of rumor spread in social networks.