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

Jinyu Cai, Jialong Li, Mingyue Zhang, Munan Li, Chen-Shu Wang, Kenji Tei

TL;DR

This work tackles how language evolves under social media regulation by building an LLM-driven multi-agent system with supervisory and participant agents. The framework uses Memory, Reflection, and Planning modules to iteratively evolve communication strategies that aim to evade oversight while preserving information fidelity, evaluated across abstract and realistic scenarios. Key findings show that LLMs can develop nuanced, indirect encoding strategies, with GPT-4 generally achieving faster convergence and higher stability than GPT-3.5, though information accuracy remains imperfect in some contexts. The study provides insights for understanding language evolution under constraint, informs moderation policy design, and offers an open-source simulation platform for further research.

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.

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

TL;DR

This work tackles how language evolves under social media regulation by building an LLM-driven multi-agent system with supervisory and participant agents. The framework uses Memory, Reflection, and Planning modules to iteratively evolve communication strategies that aim to evade oversight while preserving information fidelity, evaluated across abstract and realistic scenarios. Key findings show that LLMs can develop nuanced, indirect encoding strategies, with GPT-4 generally achieving faster convergence and higher stability than GPT-3.5, though information accuracy remains imperfect in some contexts. The study provides insights for understanding language evolution under constraint, informs moderation policy design, and offers an open-source simulation platform for further research.

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.
Paper Structure (27 sections, 7 figures)

This paper contains 27 sections, 7 figures.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Large Language Models
  4. Slang Detection and Identification
  5. Evolutionary Game and Social Simulation with LLMs
  6. Framework Design
  7. Overview
  8. Participant Agents
  9. Dialogue Module
  10. Memory Module
  11. Reflection Module
  12. Planning Module
  13. Supervisory Agent
  14. Similarities and Differences between Our Framework and Evolutionary Computing
  15. Evaluation
  16. ...and 12 more sections

Figures (7)

  • Figure 1: Overview of Language Evolution Simulation System. The system comprises two main types of agents: the Participant and the Supervisor. The Participant agent uses a Planning Module to create a communication plan based on background information, regulations, and guidance. This plan is then executed in the Dialogue Module, where the LLM crafts dialogue content to discreetly convey specific information while evading detection by the Supervisor. The Memory Module retains dialogue history and violation records, providing a reference for the LLM to maintain dialogue consistency and learn from past mistakes. The Reflection Module, triggered at the start and end of dialogue cycles, analyzes the dialogue and violation logs to formulate new regulations or guidance for improving future communications. The Supervisor evaluates dialogues for compliance with set rules. This system dynamically refines its communication approach through continuous feedback and self-improvement mechanisms. The examples shown utilize a Guessing Numbers Scenario.
  • Figure 2: Scenario 1: Evolution of dialogue turns and accuracy metrics for GPT-3.5 and GPT-4."Turn count" in (a, b) refers to the number of turns in a conversation where each agent sends a message once per turn and the participant Agent successfully exchanges information without being detected by the supervising Agent (higher is better)."Accuracy" in (c,d) refer to the degree of precision between the guessed value and the true value.
  • Figure 3: Scenario 2: Pet trading dialogue dynamics and success rate comparison for GPT-3.5 and GPT-4. The "success count" in (c,d) refers to the number of instances where the information obtained during the interview matches the original information provided to the LLM agent.
  • Figure 4: Scenario 3: Trends in forum discussion engagement on ALPS-Treated water issue. "Dialogue attempt count" in (a,b) refer to the number of rounds the agents attempted to converse(lower is better).
  • Figure 5: Sample dialogue in Scenario 1 (via GPT-3.5)
  • ...and 2 more figures