  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.
