The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents
Yuhan Liu, Zirui Song, Juntian Zhang, Xiaoqing Zhang, Xiuying Chen, Rui Yan
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 FUSE ( F ake news evol U tion S imulation fram 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: spreaders who propagate information, commentators who provide interpretations, verifiers who fact-check, and standers who observe passively to simulate realistic daily interactions that progressively distort true news. To quantify these gradual distortions, we develop 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
BibTeX
@inproceedings{emnlp2025_thestepwisedecep,
title = {The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents},
author = {Yuhan Liu and Zirui Song and Juntian Zhang and Xiaoqing Zhang and Xiuying Chen and Rui Yan},
booktitle = {EMNLP 2025},
year = {2025}
}