What question did this study set out to answer?

The research aims to create synthetic social media data for training on social-cybersecurity evaluations.

June 20, 2026Open Access

Synthetic social media creation for social-cybersecurity training

MHMatthew HicksCarnegie Mellon University MFMohamed FaragCarnegie Mellon University KCKathleen CarleyCarnegie Mellon University

Key Points

The research aims to create synthetic social media data for training on social-cybersecurity evaluations.
Developed an agent-based simulation, SynX, utilizing the AESOP tool.
Generated synthetic data using a large language model for X/Twitter API.
Evaluated performance using network metrics and the BEND framework.
Synthetic social media data accurately reflects real-world interactions.
Measured improvements in evaluating social-cybersecurity maneuvers in training environments.
Achieved reliable outputs as per network metrics and BEND framework assessments.

Abstract

Abstract This paper discusses the creation of realistic, dynamic, and controllable synthetic social media data to support instruction on evaluating social-cybersecurity maneuvers in social media. We propose an agent-based simulation called SynX that takes as input the scenario templates created by Netanomics’ AI-Enabled Scenario Orchestration and Planning (AESOP) tool and outputs an X/Twitter API v1 message corpus by leveraging a large language model (LLM). We conduct an experiment on LLM prompting and evaluate the output of SynX using network metrics and the BEND framework.

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