Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms

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Bibliographic Details
Main Authors:	Cai, Jinyu, Ishimizu, Yusei, Zhang, Mingyue, Li, Munan, Li, Jialong, Tei, Kenji
Format:	Preprint
Published:	2025
Subjects:	Social and Information Networks Artificial Intelligence Neural and Evolutionary Computing
Online Access:	Acceder al recurso
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_version_	1866929732699815936
author	Cai, Jinyu Ishimizu, Yusei Zhang, Mingyue Li, Munan Li, Jialong Tei, Kenji
author_facet	Cai, Jinyu Ishimizu, Yusei Zhang, Mingyue Li, Munan Li, Jialong Tei, Kenji
contents	Social media platforms frequently impose restrictive policies to moderate user content, prompting the emergence of creative evasion language strategies. This paper presents a multi-agent framework based on Large Language Models (LLMs) to simulate the iterative evolution of language strategies under regulatory constraints. In this framework, participant agents, as social media users, continuously evolve their language expression, while supervisory agents emulate platform-level regulation by assessing policy violations. To achieve a more faithful simulation, we employ a dual design of language strategies (constraint and expression) to differentiate conflicting goals and utilize an LLM-driven GA (Genetic Algorithm) for the selection, mutation, and crossover of language strategies. The framework is evaluated using two distinct scenarios: an abstract password game and a realistic simulated illegal pet trade scenario. Experimental results demonstrate that as the number of dialogue rounds increases, both the number of uninterrupted dialogue turns and the accuracy of information transmission improve significantly. Furthermore, a user study with 40 participants validates the real-world relevance of the generated dialogues and strategies. Moreover, ablation studies validate the importance of the GA, emphasizing its contribution to long-term adaptability and improved overall results.
format	Preprint
id	arxiv_https___arxiv_org_abs_2502_19193
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms Cai, Jinyu Ishimizu, Yusei Zhang, Mingyue Li, Munan Li, Jialong Tei, Kenji Social and Information Networks Artificial Intelligence Neural and Evolutionary Computing Social media platforms frequently impose restrictive policies to moderate user content, prompting the emergence of creative evasion language strategies. This paper presents a multi-agent framework based on Large Language Models (LLMs) to simulate the iterative evolution of language strategies under regulatory constraints. In this framework, participant agents, as social media users, continuously evolve their language expression, while supervisory agents emulate platform-level regulation by assessing policy violations. To achieve a more faithful simulation, we employ a dual design of language strategies (constraint and expression) to differentiate conflicting goals and utilize an LLM-driven GA (Genetic Algorithm) for the selection, mutation, and crossover of language strategies. The framework is evaluated using two distinct scenarios: an abstract password game and a realistic simulated illegal pet trade scenario. Experimental results demonstrate that as the number of dialogue rounds increases, both the number of uninterrupted dialogue turns and the accuracy of information transmission improve significantly. Furthermore, a user study with 40 participants validates the real-world relevance of the generated dialogues and strategies. Moreover, ablation studies validate the importance of the GA, emphasizing its contribution to long-term adaptability and improved overall results.
title	Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms
topic	Social and Information Networks Artificial Intelligence Neural and Evolutionary Computing
url	https://arxiv.org/abs/2502.19193