Simulating human responses to environmental messaging

Abstract This paper presents ongoing work to implement and evaluate virtual humans whose responses to environmental messaging are shaped by their media diets and social interactions. The project scraped thousands of social media post-comment pairs related to environmental issues, classified them by viewpoint through the large-scale orchestration of multiple instances of large language models, and built a vector database of embedded interactions with associated classification metadata to serve as