🤖 AI Summary
This study investigates whether large language models (LLMs) can faithfully simulate emotional diffusion patterns observed in real online communities—specifically Reddit. Method: We construct LLM-generated multi-turn dialogue graphs and systematically compare them against empirical Reddit social graphs across four dimensions: structural connectivity, interaction recurrence, affective dynamics, and community emergence. We integrate VADER and BERT-based sentiment analysis with graph-behavior joint modeling to quantify discrepancies. Contribution/Results: We provide the first empirical evidence that LLM-simulated graphs consistently exhibit linear, isolated chain structures and monotonic sentiment trajectories—lacking the high-density connectivity, sentiment reversals, and heterogeneous evolution characteristic of real networks. Consequently, LLM simulation induces significant reduction in affective diversity and severe class imbalance, degrading downstream graph prediction performance. This work reveals fundamental structural limitations of current LLMs in modeling socio-dynamic processes and establishes a critical evaluation benchmark and actionable direction for trustworthy, AI-driven social simulation.
📝 Abstract
Understanding how emotions diffuse through social networks is central to computational social science. Recently, large language models (LLMs) have been increasingly used to simulate social media interactions, raising the question of whether LLM-generated data can realistically reproduce emotion diffusion patterns observed in real online communities. In this study, we conduct a systematic comparison between emotion diffusion in real-world social graphs and in LLM-simulated interaction networks. We construct diffusion graphs from Reddit discussion data and compare them with synthetic social graphs generated through LLM-driven conversational simulations. Emotion states are inferred using established sentiment analysis pipelines, and both real and simulated graphs are analyzed from structural, behavioral, and predictive perspectives. Our results reveal substantial structural and dynamic discrepancies between real and simulated diffusion processes. Real-world emotion diffusion exhibits dense connectivity, repeated interactions, sentiment shifts, and emergent community structures, whereas LLM-simulated graphs largely consist of isolated linear chains with monotonic emotional trajectories. These structural limitations significantly affect downstream tasks such as graph-based emotion prediction, leading to reduced emotional diversity and class imbalance in simulated settings. Our findings highlight current limitations of LLM-based social simulation in capturing the interactive complexity and emotional heterogeneity of real social networks. This work provides empirical evidence for the cautious use of LLM-generated data in social science research and suggests directions for improving future simulation frameworks.