The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents

TL;DR

A novel Large Language Model-based simulation approach explicitly focusing on fake news evolution from real news, which effectively captures fake news evolution patterns and accurately reproduces known fake news, aligning closely with human evaluations.

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 (Fake news evolUtion Simulation framEwork), 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 bystanders 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.

Figures (8)

• Figure 1: (a) Macro-level observation of population dynamics based on the mathematical model, categorizing individuals into four types and showing their quantity changes over time. (b) The micro-level conventional fake news dissemination model assumes that fake news inherently exists. (c) Micro-level evolution of fake news, where true news gradually evolves into fake news during network propagation with content alterations at various stages.
• Figure 2: Our FUSE framework simulates news evolution by equipping each agent with role-based decision-making capabilities. Propagation Role-aware agents (PRA) process true news through interactions within the news evolution simulator (NES), where their role identities shape how they engage with the news.
• Figure 3: (a) The FUSE-EVAL scores show cumulative information deviations over time. (b) A Case of FUSE: True news gradually evolves into partially false and eventually entirely fake news over time.
• Figure 4: (a) Ablation study showing the effectiveness of hierarchical memory and propagation roles. (b) Impact of removing different agent types on fake news evolution. (c) Effectiveness of early intervention, showing an apparent reduction in deviation over time compared to the no-intervention condition.
• Figure 5: (a) The contribution percentages of factors in FUSE-EVAL to fake news evolution. (b) Comparison of the contributions of these factors across different topics, with Politics and Terrorism showing balanced contributions, while Science relies more on NII and less on TS and STS.