Agent-Based Simulation of Social Networks for Disinformation Research
Analysis
This paper addresses the challenges of studying online social networks (OSNs) by proposing a simulation framework. The framework's key strength lies in its realism and explainability, achieved through agent-based modeling with demographic-based personality traits, finite-state behavioral automata, and an LLM-powered generative module for context-aware posts. The integration of a disinformation campaign module (red module) and a Mastodon-based visualization layer further enhances the framework's utility for studying information dynamics and the effects of disinformation. This is a valuable contribution because it provides a controlled environment to study complex social phenomena that are otherwise difficult to analyze due to data limitations and ethical concerns.
Key Takeaways
- •Proposes an agent-based simulation framework for OSNs.
- •Employs LLMs for generating realistic social media posts.
- •Includes a module for simulating disinformation campaigns.
- •Offers a Mastodon-based visualization layer for real-time inspection.
- •Aims to provide a controllable environment for studying information dynamics and disinformation.
“The framework enables the creation of customizable and controllable social network environments for studying information dynamics and the effects of disinformation.”