Language Evolution for Evading Social Media Regulation via LLM-based Multi-agent Simulation

TL;DR

This paper introduces a multi-agent simulation framework using Large Language Models to study how user language evolves on social media under strict regulation, revealing adaptive communication strategies and language dynamics.

Contribution

It presents a novel LLM-based multi-agent simulation framework to explore language evolution in regulated social media environments, a previously underexplored area.

Findings

LLMs can simulate nuanced language dynamics under constraints.

Agents develop different evasion strategies across scenarios.

Language evolution improves evasion and information accuracy.

Abstract

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.