{"ID":2842314,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10384","arxiv_id":"2511.10384","title":"Simulating Misinformation Propagation in Social Networks using Large Language Models","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.10384","url_pdf":"https://arxiv.org/pdf/2511.10384v1","authors":"[\"Raj Gaurav Maurya\",\"Vaibhav Shukla\",\"Raj Abhijit Dandekar\",\"Rajat Dandekar\",\"Sreedath Panat\"]","published":"2025-11-13T15:01:19Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"cs.AI\",\"cs.CL\",\"cs.CY\"]","methods":"[\"Diffusion Model\",\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}