@inproceedings{liu-etal-2025-stepwise,
    title = "The Stepwise Deception: Simulating the Evolution from True News to Fake News with {LLM} Agents",
    author = "Liu, Yuhan  and
      Song, Zirui  and
      Zhang, Juntian  and
      Zhang, Xiaoqing  and
      Chen, Xiuying  and
      Yan, Rui",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1330/",
    pages = "26187--26203",
    ISBN = "979-8-89176-332-6",
    abstract = "With the growing spread of misinformation online, understanding how true news evolves into fake news has become crucial for early detection and prevention. However, previous research has often assumed fake news inherently exists rather than exploring its gradual formation. To address this gap, we propose $\textbf{FUSE}$ ($\textbf{F}$ake news evol$\textbf{U}$tion $\textbf{S}$imulation fram$\textbf{E}$work), a novel Large Language Model (LLM)-based simulation approach explicitly focusing on fake news evolution from real news. Our framework model a social network with four distinct types of LLM agents commonly observed in daily interactions: $\textit{ spreaders}$ who propagate information, $\textit{commentators}$ who provide interpretations, $\textit{verifiers}$ who fact-check, and $\textit{standers}$ who observe passively to simulate realistic daily interactions that progressively distort true news. To quantify these gradual distortions, we develop $\textbf{FUSE-EVAL}$, a comprehensive evaluation framework measuring truth deviation along multiple linguistic and semantic dimensions. Results show that FUSE effectively captures fake news evolution patterns and accurately reproduces known fake news, aligning closely with human evaluations. Experiments demonstrate that FUSE accurately reproduces known fake news evolution scenarios, aligns closely with human judgment, and highlights the importance of timely intervention at early stages. Our framework is extensible, enabling future research on broader scenarios of fake news:https://github.com/LiuYuHan31/FUSE"
}