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DET: Detection Evasion Techniques of State-Sponsored Accounts

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  • Jacobs, Charity S.
  • Ng, Hui Xian Lynnette
  • Carley, Kathleen M.

Abstract

This study analyzes two covert Chinese bot networks, employing tweet-based and account-based methods to find detection evasion tactics. We reveal the use of message artifacts that disguise spam, engagement strategies that mimic human interaction, and behavioral patterns suggesting algorithmic control. We uncover bot maintenance practices and algorithmic account naming conventions. These insights demonstrate the evolving strategies of inauthentic digital personas, enhance our understanding of online disinformation campaigns, and inform the development of digital manipulation countermeasures. Comparing campaigns in 2021 and 2023, we discover that the techniques used by state-sponsored actors shifted from text-based to image-based techniques, indicating the increased sophistication of these actors to evade the detection algorithms of the social media platform. This work provides insight into the tactics of covert bot networks and discusses possible advancements in detection techniques.

Suggested Citation

  • Jacobs, Charity S. & Ng, Hui Xian Lynnette & Carley, Kathleen M., 2024. "DET: Detection Evasion Techniques of State-Sponsored Accounts," OSF Preprints kzjbg, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:kzjbg
    DOI: 10.31219/osf.io/kzjbg
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    References listed on IDEAS

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    1. King, Gary & Pan, Jennifer & Roberts, Margaret E., 2017. "How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument," American Political Science Review, Cambridge University Press, vol. 111(3), pages 484-501, August.
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