# Simulating Misinformation Propagation in Social Networks using Large Language Models

arXiv: https://arxiv.org/abs/2511.10384 · HTML: https://mlsanity.com/paper/2511.10384/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2025.

## Built on by (most cited first, 4 of 4)

- [Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems](https://mlsanity.com/paper/2601.21963/) (2026)
- [Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse](https://mlsanity.com/paper/2605.21540/) (2026)
- [LLMs for Social Network Modeling: From Network Generation to Dynamic Processes](https://mlsanity.com/paper/2609.08049/) (2026)
- [Disentangling Models from Personas in Heterogeneous LLM Simulations](https://mlsanity.com/paper/2610.07535/) (2026)

## Abstract (arXiv)

Misinformation on social media thrives on surprise, emotion, and identity-driven reasoning, often amplified through human cognitive biases. To investigate these mechanisms, we model large language model (LLM) personas as synthetic agents that mimic user-level biases, ideological alignments, and trust heuristics. Within this setup, we introduce an auditor--node framework to simulate and analyze how misinformation evolves as it circulates through networks of such agents. News articles are propagated across networks of persona-conditioned LLM nodes, each rewriting received content. A question--answering-based auditor then measures factual fidelity at every step, offering interpretable, claim-level tracking of misinformation drift. We formalize a misinformation index and a misinformation propagation rate to quantify factual degradation across homogeneous and heterogeneous branches of up to 30 sequential rewrites. Experiments with 21 personas across 10 domains reveal that identity- and ideology-based personas act as misinformation accelerators, especially in politics, marketing, and technology. By contrast, expert-driven personas preserve factual stability. Controlled-random branch simulations further show that once early distortions emerge, heterogeneous persona interactions rapidly escalate misinformation to propaganda-level distortion. Our taxonomy of misinformation severity -- spanning factual errors, lies, and propaganda -- connects observed drift to established theories in misinformation studies. These findings demonstrate the dual role of LLMs as both proxies for human-like biases and as auditors capable of tracing information fidelity. The proposed framework provides an interpretable, empirically grounded approach for studying, simulating, and mitigating misinformation diffusion in digital ecosystems.