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.

翻译：诸如Twitter、Reddit和微博等社交媒体平台在全球交流中发挥着至关重要的作用，但在地缘政治敏感地区往往受到严格监管。这种情况促使用户巧妙地改变他们的交流方式，时常在这些受监管的社交媒体环境中使用编码语言。这种交流方式的转变不仅仅是应对监管的一种策略，更是语言演化的生动体现，展示了语言如何在人类社会和技术压力下自然演变。研究受监管社交媒体语境下的语言演变，对于保障言论自由、优化内容审核以及推进语言学研究具有重要意义。本文提出了一种基于大语言模型的多智能体模拟框架，旨在探索受监管社交媒体环境中用户语言的演变。该框架采用大语言模型驱动的智能体：执行对话监管的监管智能体，以及在对话中不断演化语言策略的参与智能体，通过模拟在旨在规避社交媒体监管的严格规定下交流风格的演变过程。本研究通过从抽象场景到现实情境的一系列场景评估了该框架的有效性。关键发现表明，大语言模型能够模拟受限环境中微妙的语言动态和交互，随着演化的推进，在规避监管和信息准确性方面均有所提升。此外，研究发现大语言模型智能体会针对不同场景采用不同的策略。