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Interoperable pipelines for social cyber-security: assessing Twitter information operations during NATO Trident Juncture 2018

  • Joshua UyhengEmail author
  • Thomas Magelinski
  • Ramon Villa-Cox
  • Christine Sowa
  • Kathleen M. Carley
S.I.: Social Cyber-Security
  • 79 Downloads

Abstract

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.

Keywords

Social cyber-security Information operations Interoperability 

Notes

Acknowledgements

This work is supported in part by the Office of Naval Research under the Multidisciplinary University Research Initiatives (MURI) Program award number N000141712675 Near Real Time Assessment of Emergent Complex Systems of Confederates, BotHunter award number N000141812108, award number N00014182106 Group Polarization in Social Media: An Effective Network Approach to Communicative Reach and Disinformation, and award number N000141712605 Developing Novel Socio-computational Methodologies to Analyze Multimedia-based Cyber Propaganda Campaigns. This work is also supported by the center for Computational Analysis of Social and Organizational Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or the U.S. government. Additionally, Thomas Magelinski was supported by an ARCS foundation scholarship.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Computational Analysis of Social and Organizational Systems (CASOS), Institute for Software ResearchCarnegie Mellon UniversityPittsburghUSA

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