Emotion Diffusion in Real and Simulated Social Graphs: Structural Limits of LLM-Based Social Simulation

TL;DR

This paper compares emotion diffusion in real social networks with LLM-simulated networks, revealing significant structural and dynamic differences that impact the realism and utility of LLM-based social simulations.

Contribution

It systematically analyzes and highlights the structural and behavioral limitations of LLM-generated social graphs in replicating real-world emotion diffusion patterns.

Findings

Real networks show dense connectivity and community structures.

Simulated graphs tend to be linear chains with monotonic emotions.

Structural differences reduce emotional diversity and prediction accuracy.

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.