In the digital era, the rapid propagation of fake news and rumors via social networks brings notable societal challenges and impacts public opinion regulation. Traditional fake news modeling typically forecasts the general popularity trends of different groups or numerically represents opinions shift. However, these methods often oversimplify real-world complexities and overlook the rich semantic information of news text. The advent of large language models (LLMs) provides the possibility of modeling subtle dynamics of opinion. Consequently, in this work, we introduce a Fake news Propagation Simulation framework (FPS) based on LLM, which studies the trends and control of fake news propagation in detail. Specifically, each agent in the simulation represents an individual with a distinct personality. They are equipped with both short-term and long-term memory, as well as a reflective mechanism to mimic human-like thinking. Every day, they engage in random opinion exchanges, reflect on their thinking, and update their opinions. Our simulation results uncover patterns in fake news propagation related to topic relevance, and individual traits, aligning with real-world observations. Additionally, we evaluate various intervention strategies and demonstrate that early and appropriately frequent interventions strike a balance between governance cost and effectiveness, offering valuable insights for practical applications. Our study underscores the significant utility and potential of LLMs in combating fake news.

翻译：在数字时代，虚假新闻和谣言通过社交网络的快速传播带来了显著的社会挑战，并影响了公众舆论监管。传统的虚假新闻建模通常预测不同群体的一般流行趋势，或以数值方式表示观点转变。然而，这些方法往往过度简化现实世界的复杂性，并忽视了新闻文本丰富的语义信息。大语言模型（LLM）的出现为模拟观点的微妙动态提供了可能性。因此，在本工作中，我们引入了一种基于LLM的虚假新闻传播模拟框架（FPS），详细研究了虚假新闻传播的趋势与控制。具体而言，模拟中的每个智能体代表一个具有独特个性的个体。它们配备了短期和长期记忆，以及模拟人类思维的反省机制。每天，它们进行随机的观点交流，反思自身思考，并更新观点。我们的模拟结果揭示了与主题相关性和个体特质相关的虚假新闻传播模式，与现实世界观察一致。此外，我们评估了多种干预策略，并证明早期且适度频繁的干预在治理成本与效果之间取得了平衡，为实际应用提供了宝贵的见解。我们的研究强调了LLM在打击虚假新闻中的重要效用与潜力。