arXiv subjects
Publications and source records attributed to David Montoro Aguilera.
This article evaluates AI-enabled influence operations in synthetic social networks through controlled simulations of narrative release, amplification, and counter-messaging. We measure exposure and belief change in agentic audiences, showing that amplification maximizes reach, counter-messaging shifts opinions most, and narrative release requires larger attacker footprints.
Research on online social networks (OSNs) is often hindered by platform opacity, limited access to data, and ethical constraints. Simulation offer a valuable alternative, but existing frameworks frequently lack realism and explainability. This paper presents a simulation framework that models synthetic social networks with agents endowed with demographic-based personality traits and finite-state behavioral automata, enabling realistic and interpretable actions. A generative module powered by a large language model (LLM) produces context-aware social media posts consistent with each agent's profile and memory. In parallel, a red module implements DISARM-inspired workflows to orchestrate disinformation campaigns executed by malicious agents targeting simulated audiences. A Mastodon-based visualization layer supports real-time inspection and post-hoc validation of agent activity within a familiar interface. We evaluate the resulting synthetic social networks using topological metrics and LLM-based content assessments, demonstrating structural, behavioral, and linguistic realism. Overall, the framework enables the creation of customizable and controllable social network environments for studying information dynamics and the effects of disinformation.