Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms
arXiv:2502.19193v2 Announce Type: replace-cross Abstract: 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
The increasing sophistication of Large Language Models and their integration into social media, coupled with ongoing platform moderation challenges, makes the simulation of their interaction timely.
This research provides insights into the co-evolution of AI-generated content and platform regulation, which is critical for understanding future information environments and societal impacts.
Our understanding of how language evolves under AI influence and regulatory pressure is enhanced, potentially leading to new strategies for platform moderation and user expression.
- · Social media platforms
- · AI ethicists
- · NLP researchers
- · Regulatory bodies
- · Unregulated content creators
- · Censorship circumvention tools
AI models will quickly adapt to and generate 'evasion' language strategies in response to moderation policies.
Social media platforms will need increasingly sophisticated AI-driven moderation systems to keep pace with evolving 'evasion' language.
The arms race between generative AI and moderation AI could lead to a highly obfuscated online discourse, difficult for humans to fully interpret.
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Read at arXiv cs.AI