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<p>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.</p>

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Synthetic social media creation for social-cybersecurity training

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

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  • Matthew Hicks,
  • Mohamed Farag,
  • Kathleen Carley

摘要

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.