Social cyber-security is an emergent field defining a multidisciplinary and multimethodological approach to studying and preserving the free and open exchange of information online. This work contributes to burgeoning scholarship in this field by advocating the use of interoperable pipelines of computational tools. We demonstrate the utility of such a pipeline in a case study of Twitter information operations during the NATO Trident Juncture Exercises in 2018. By integratively utilizing tools from machine learning, natural language processing, and dynamic network analysis, we uncover significant bot activity aiming to discredit NATO targeted to key allied nations. We further show how to extend such analysis through drill-down procedures on individual influencers and influential subnetworks. We reflect on the value of interoperable pipelines for accumulating and triangulating insights that enable social cyber-security analysts to draw relevant insights across various scales of granularity.

社会网络安全是一个新兴领域，它定义了一种多学科和多方法的方法来研究和维护在线自由和开放的信息交换。这项工作提倡使用可互操作的计算工具管道，从而为该领域的新兴学术贡献力量。我们在2018年北约三叉戟联合演习期间的Twitter信息操作案例研究中证明了这种管道的实用性。通过综合利用机器学习，自然语言处理和动态网络分析中的工具，我们发现了旨在破坏信誉的重要机器人活动北约的目标是主要盟国。我们还将进一步展示如何通过对单个影响者和有影响力的子网进行深入分析来扩展此类分析。