This study examines how social media users adapt their communication strategies in geopolitically sensitive areas with strict regulations. Through a multi-agent simulation framework utilizing Large Language Models, the research analyzes how individuals modify their language to avoid censorship. It highlights the importance of this evolution for freedom of speech and content moderation while illustrating the complex dynamics at play. Results indicate that LLM agents effectively simulate nuanced interactions and can evolve their language strategies based on diverse scenarios, showing improvements in evading supervision and maintaining information accuracy.
Social media users, in response to strict regulations, have evolved the manner in which they communicate, utilizing coded language and showcasing a natural progression in linguistic development under pressure.
The evolution of language in regulated environments represents not just a reaction to constraints but an illustration of how societal and technological factors influence communication methods over time.
Through a multi-agent simulation framework driven by Large Language Models, we explore the dynamics of language evolution within regulated social media, demonstrating effective adaptation strategies by users.
Our findings underline that LLM-powered agents successfully replicate the nuanced interactions indicative of constrained communication, adapting their strategies based on contextual scenarios while improving evasion techniques and information delivery.
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