IDEAS home Printed from https://ideas.repec.org/a/taf/cdanxx/v41y2025i2p319-335.html
   My bibliography  Save this article

Recruit and threaten: hate speech detection within the pro-Wagner digital ecosystem on Telegram

Author

Listed:
  • Giulia Porrino
  • Federico Borgonovo
  • Matteo Arru

Abstract

The PMC Wagner emerged as a central actor in the Russian power projection. This research consists of an evidence-based analysis to grasp the relationships within the pro-Wagner digital ecosystem. Through Socio-Semantic Network Analysis and hate-speech detection using a machine learning model, the study aims to map the PMC Wagner on Telegram and reconstruct its morphological characteristics. The study demonstrates that some intermediaries are essential in spreading propaganda due to a combination of their position and the violence of their discourse. Targeting the videos of the most relevant intermediaries is an efficient strategy to prevent other violent extremist actors.

Suggested Citation

  • Giulia Porrino & Federico Borgonovo & Matteo Arru, 2025. "Recruit and threaten: hate speech detection within the pro-Wagner digital ecosystem on Telegram," Defense & Security Analysis, Taylor & Francis Journals, vol. 41(2), pages 319-335, April.
    • Handle: RePEc:taf:cdanxx:v:41:y:2025:i:2:p:319-335
    DOI: 10.1080/14751798.2024.2411772
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14751798.2024.2411772
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14751798.2024.2411772?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:cdanxx:v:41:y:2025:i:2:p:319-335. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CDAN20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.