Who could be behind QAnon? Authorship attribution with supervised machine-learning.

A series of social media posts on 4chan then 8chan, signed under the pseudonym 'Q', started a movement known as QAnon, which led some of its most radical supporters to violent and illegal actions. To identify the person(s) behind Q, we evaluate the coincidence between the linguistic properties of th...

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Publicado en:Digital Scholarship in the Humanities Vol. 38; no. 4; pp. 1418 - 1431
Autores principales: Cafiero, Florian, Camps, Jean-Baptiste
Formato: Artículo
Publicado: Oxford University Press / USA Dec2023
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Who could be behind QAnon? Authorship attribution with supervised machine-learning.
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        au:
          Cafiero, Florian
          Camps, Jean-Baptiste
        affil:
          Sciences Po, médialab , 27 Rue Saint Guillaume , France
          École Nationale des Chartes, Université Paris, Sciences & Lettres , 65 rue de Richelieu , France
      su:
        Attribution of authorship
        Machine learning
        QAnon
        Coincidence
        Literary form
        Social media
      sug:
        subj:
          Attribution of authorship
          Machine learning
          QAnon
          Coincidence
          Literary form
          Social media
      keyword:
        Authorship attribution
        Computational forensics
        Conspiracy theories
        Domestic threats and terrorism
      ab: A series of social media posts on 4chan then 8chan, signed under the pseudonym 'Q', started a movement known as QAnon, which led some of its most radical supporters to violent and illegal actions. To identify the person(s) behind Q, we evaluate the coincidence between the linguistic properties of the texts written by Q and to those written by a list of suspects provided by journalistic investigation. To identify the authors of these posts, serious challenges have to be addressed. The 'Q drops' are very short texts, written in a way that constitute a sort of literary genre in itself, with very peculiar features of style. These texts might have been written by different authors, whose other writings are often hard to find. After an online ethnography of the movement, necessary to collect enough material written by these thirteen potential authors, we use supervised machine learning to build stylistic profiles for each of them. We then performed a 'rolling analysis', looking repeatedly through a moving window for parts of Q's writings matching our profiles. We conclude that two different individuals, Paul F. and Ron W. are the closest match to Q's linguistic signature, and they could have successively written Q's texts. These potential authors are not high-ranked personality from the US administration, but rather social media activists.
      pubtype: Academic Journal
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    language: English
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