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Article published In:
Journal of Language and Politics
Vol. 22:6 (2023) ► pp.894–917

A meta-discursive analysis of engagement markers in QAnon anti-immigration comments

ORCID logo | Universiti MalayaKuala Lumpur
ORCID logo | Rabdan AcademyAbu Dhabi
ORCID logo | Universiti MalayaKuala Lumpur
ORCID logo | Universiti MalayaKuala Lumpur
ORCID logo | Universiti MalayaKuala Lumpur
To better understand QAnon’s anti-immigration rhetoric, the study conducted a meta-discursive analysis of one of the group’s active Telegram channels by drawing on Hyland’s (2005) model of interaction. Specifically, engagement markers in their immigration-related discourse were analyzed to see how they contribute to endorsing the group’s macro conspiratorial arguments. The results illustrate a complex rhetorical manipulation and nearly unanimous expression of hate toward immigrants, with the use of ‘directives’ and ‘questions’ being the most prevalent metadiscursive markers. The former suggests that the commenters were more assertive or commanding in their tone, while the latter located within rhetorical persuasion, aimed at stimulating negative attitude toward immigrants. Conversely, a low frequency of the ‘shared knowledge’ markers indicates a lack of interest to establish credibility and constructive dialogue with the audience. The study aimed to unpack the nature of anti-immigration extremist discourse on social media, and its potential to incite violence among public.
Keywords: QAnon, conspiracy theory, anti-immigration, Telegram, engagement markers, metadiscursive analysis
Article outline
  • 1.Introduction
  • 2.QAnon and political mainstreaming
    • 2.1Mediated radicalisation
  • 3.The discourse of anti-immigration
  • 4.Data and method
    • 4.1Engagement analysis
  • 5.Findings
  • 6.Discussion and conclusion
  • Notes
  • References
Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.

For any use beyond this license, please contact the publisher at [email protected].

Published online: 22 September 2023
DOI logo
https://doi.org/10.1075/jlp.23038.ras
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