Intelligent Digital Twin for Predicting Technology Discourse Patterns: Agent-Based Modeling of User Interactions and Sentiment Dynamics in DeepSeek Discourse Case

Abstract

Understanding user interaction patterns during technology-triggered public discourse provides critical insights into how emerging technologies gain social meaning. This study develops an intelligent digital twin framework for modeling discourse dynamics around DeepSeek, an indigenous large language model that generated approximately 250,000 social media interactions during a 13-day period. By integrating LLM-enhanced semantic analysis with agent-based modeling, we create a comprehensive virtual representation that captures both content characteristics and behavioral dynamics. Our analysis identifies six distinct thematic domains that structure public engagement: Technological Competition, Technological Breakthrough, User Feedback, Financial Market, Social Influence, and Information Security. The agent-based simulation reveals distinctive participation and sentiment patterns across different user segments, with general users expressing stronger positive sentiments than domain experts and institutional accounts. Network analysis demonstrates the evolution from random-like initial connection patterns to scale-free structures with pronounced influence hubs. The simulation results illuminate how individual behaviors aggregate to produce complex discourse patterns, offering insights into the micro-mechanisms underlying technology reception. This research advances digital twin methodologies beyond physical systems into social phenomena, providing a framework for anticipating public responses to technological innovations and informing more effective communication strategies.