# Simulating Online Social Media Conversations on Controversial Topics Using AI Agents Calibrated on Real-World Data

arXiv: https://arxiv.org/abs/2509.18985 · HTML: https://mlsanity.com/paper/2509.18985/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks](https://mlsanity.com/paper/2603.15809/) (2026)
- [Simulating and Experimenting with Social Media Mobilization Using LLM Agents](https://mlsanity.com/paper/2510.26494/) (2025)
- [Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models](https://mlsanity.com/paper/2511.03699/) (2025)
- [LLMs for Social Network Modeling: From Network Generation to Dynamic Processes](https://mlsanity.com/paper/2609.08049/) (2026)
- [Illusory Truth or Mere Exposure? Model-Dependent Repetition Effects in LLM-Based Social Media Simulations](https://mlsanity.com/paper/2609.36278/) (2026)

## Abstract (arXiv)

Online social networks offer a valuable lens to analyze both individual and collective phenomena. Researchers often use simulators to explore controlled scenarios, and the integration of Large Language Models (LLMs) makes these simulations more realistic by enabling agents to understand and generate natural language content. In this work, we investigate the behavior of LLM-based agents in a simulated microblogging social network. We initialize agents with realistic profiles calibrated on real-world online conversations from the 2022 Italian political election and extend an existing simulator by introducing mechanisms for opinion modeling. We examine how LLM agents simulate online conversations, interact with others, and evolve their opinions under different scenarios. Our results show that LLM agents generate coherent content, form connections, and build a realistic social network structure. However, their generated content displays less heterogeneity in tone and toxicity compared to real data. We also find that LLM-based opinion dynamics evolve over time in ways similar to traditional mathematical models. Varying parameter configurations produces no significant changes, indicating that simulations require more careful cognitive modeling at initialization to replicate human behavior more faithfully. Overall, we demonstrate the potential of LLMs for simulating user behavior in social environments, while also identifying key challenges in capturing heterogeneity and complex dynamics.