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

Authors: Cafiero, Florian; Camps, Jean-Baptiste;

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

Abstract

Abstract 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.

Keywords

FOS: Computer and information sciences, social networks, Computer Science - Computation and Language, authorship attribution, Qanon, Stylometry, Conspiracy Theories, [SHS.LANGUE] Humanities and Social Sciences/Linguistics, Authorship Attribution, disinformation, conspiracy theories, stylometry, QAnon, Computation and Language (cs.CL)

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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