@inproceedings{borah-etal-2026-persuasion,
    title = "Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-{LLM} Interactions",
    author = "Borah, Angana  and
      Mihalcea, Rada  and
      Perez-Rosas, Veronica",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.234/",
    pages = "5027--5053",
    ISBN = "979-8-89176-380-7",
    abstract = "Existing challenges in misinformation exposure and susceptibility vary across demographics, as some populations are more vulnerable to misinformation than others. Large language models (LLMs) introduce new dimensions to these challenges through their ability to generate persuasive content at scale and reinforcing existing biases. Our study introduces PANDORA, a framework that investigates the bidirectional persuasion dynamics between LLMs and humans when exposed to misinformative content. We use a multi-agent LLM framework to analyze the spread of misinformation under persuasion among demographic-oriented LLM agents. Our findings show that demographic factors influence LLM susceptibility, with up to 15 percentage point differences in misinformation correctness across groups. Multi-agent LLMs also exhibit echo chamber behavior, aligning with human-like group polarization patterns. Therefore, this work highlights demographic divides in misinformation dynamics and offers insights for future interventions."
}