AuraSight: Generating Realistic Social Media Data

2025-09-10

AI Summary

This study addresses the scarcity of realistic social media data, which constrains behavioral modeling and training in networked systems. To overcome this limitation, we propose a hybrid simulation framework integrating agent-based modeling with generative AI to synthesize a quasi-realistic, multi-day social media dataset—AuraSight—centered on trending cultural events. Our method introduces the AESOP-SynSM engine, which jointly models heterogeneous agent behaviors, social interaction rules, event evolution dynamics, and natural language content generation, thereby capturing both temporal dynamics and emergent collective phenomena. The resulting dataset features interpretable agent trajectories, authentic interaction patterns, and progressive topic evolution. Empirical evaluation across multiple network dynamics tasks demonstrates its effectiveness for model training and high fidelity in behavioral and structural simulation. AuraSight thus establishes a high-fidelity, controllable, and reproducible benchmark environment for social computing, misinformation溯源, and emergency response simulation.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data generation and simulation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This document details the narrative and technical design behind the process of generating a quasi-realistic set X data for a fictional multi-day pop culture episode (AuraSight). Social media post simulation is essential towards creating realistic training scenarios for understanding emergent network behavior that formed from known sets of agents. Our social media post generation pipeline uses the AESOP-SynSM engine, which employs a hybrid approach of agent-based and generative artificial intelligence techniques. We explicate choices in scenario setup and summarize the fictional groups involved, before moving on to the operationalization of these actors and their interactions within the SynSM engine. We also briefly illustrate some outputs generated and discuss the utility of such simulated data and potential future improvements.

Problem

• Generating realistic social media training data
• Simulating emergent network behavior scenarios
• Creating agent-based AI social media interactions

Innovation

• Hybrid agent-based and generative AI
• AESOP-SynSM engine for social simulation
• Generating quasi-realistic training scenarios