LLM-Agent-based Social Simulation for Attitude Diffusion

📰 ArXiv cs.AI

Researchers introduce discourse_simulator, a framework combining LLMs with agent-based modeling to simulate attitude diffusion in social networks

advanced Published 7 Apr 2026
Action Steps
  1. Combine LLMs with agent-based modeling to simulate social networks
  2. Use LLMs to generate social media posts and interpret opinions
  3. Model how ideas spread through social networks in response to events
  4. Analyze the simulation results to understand attitude diffusion
Who Needs to Know This

Data scientists and AI engineers on a team can benefit from this research to model and analyze complex social phenomena, while product managers can apply these insights to develop more effective social media strategies

Key Insight

💡 LLMs can be used to model how ideas spread through social networks and simulate attitude diffusion

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💡 Simulate attitude diffusion in social networks with LLMs & agent-based modeling!

Key Takeaways

Researchers introduce discourse_simulator, a framework combining LLMs with agent-based modeling to simulate attitude diffusion in social networks

Full Article

Title: LLM-Agent-based Social Simulation for Attitude Diffusion

Abstract:
arXiv:2604.03898v1 Announce Type: new Abstract: This paper introduces discourse_simulator, an open-source framework that combines LLMs with agent-based modelling. It offers a new way to simulate how public attitudes toward immigration change over time in response to salient events like protests, controversies, or policy debates. Large language models (LLMs) are used to generate social media posts, interpret opinions, and model how ideas spread through social networks. Unlike traditional agent-ba
Read full paper → ← Back to Reads

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