Simulating hashtag dynamics with networked groups of generative agents

Networked environments shape how information embedded in narratives influences individual and group beliefs and behavior. This raises key questions about how group communication around narrative media impacts belief formation and how such mechanisms contribute to the emergence of consensus or polarization. Language data from generative agents offer insight into how naturalistic forms of narrative interactions (such as hashtag generation) evolve in response to social rewards within networked communication settings. To investigate this, we developed an agent-based modeling and simulation framework composed of networks of interacting Large Language Model (LLM) agents. We benchmarked the simulations of four state-of-the-art LLMs against human group behaviors observed in a prior network experiment (Study 1) and against naturally occurring hashtags from Twitter (Study 2). Quantitative metrics of network coherence (e.g., entropy of a group's responses) reveal that while LLMs can approximate human-like coherence in sanitized domains (Study 1's experimental data), effective integration of background knowledge and social context in more complex or politically sensitive narratives likely requires careful and structured prompting.