Simulating Misinformation Propagation in Social Networks using Large Language Models

• URL: http://arxiv.org/abs/2511.10384v1
• Date: Fri, 14 Nov 2025 01:48:18 GMT
• Title: Simulating Misinformation Propagation in Social Networks using Large Language Models
• Authors: Raj Gaurav Maurya, Vaibhav Shukla, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat,
• Abstract summary: Misinformation on social media thrives on surprise, emotion, and identity-driven reasoning, often amplified through human cognitive biases.<n>To investigate these mechanisms, we model large language model (LLM) personas as synthetic agents that mimic user-level biases, ideological alignments, and trust misinformations.<n>Within this setup, we introduce an auditor-conditioned-node framework to simulate and analyze how misinformation evolves as it circulates through networks of such agents.
• Score: 4.285464959472458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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