  Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial
role in global communication but often encounter strict regulations in
geopolitically sensitive regions. This situation has prompted users to
ingeniously modify their way of communicating, frequently resorting to coded
language in these regulated social media environments. This shift in
communication is not merely a strategy to counteract regulation, but a vivid
manifestation of language evolution, demonstrating how language naturally
evolves under societal and technological pressures. Studying the evolution of
language in regulated social media contexts is of significant importance for
ensuring freedom of speech, optimizing content moderation, and advancing
linguistic research. This paper proposes a multi-agent simulation framework
using Large Language Models (LLMs) to explore the evolution of user language in
regulated social media environments. The framework employs LLM-driven agents:
supervisory agent who enforce dialogue supervision and participant agents who
evolve their language strategies while engaging in conversation, simulating the
evolution of communication styles under strict regulations aimed at evading
social media regulation. The study evaluates the framework's effectiveness
through a range of scenarios from abstract scenarios to real-world situations.
Key findings indicate that LLMs are capable of simulating nuanced language
dynamics and interactions in constrained settings, showing improvement in both
evading supervision and information accuracy as evolution progresses.
Furthermore, it was found that LLM agents adopt different strategies for
different scenarios.
