Disinformation Networks. A Quali-Quantitative Investigation of Antagonistic Dutch-speaking Telegram Channels

Tom Willaert, Stijn Peeters, Jasmin Seijbel, Nathalie Van Raemdonck

Onderzoeksoutput: Articlepeer review


In the field of disinformation research, the study of antagonistic networks and discourse on the messaging platform Telegram has developed into an active area of investigation. To this end, recent literature has specifically set out to map the scale, scope, and narrative trends marking Telegram communities with ties to localized, European contexts. The present paper contributes to this line of inquiry by offering an empirically-informed exploration of far-right and conspiracist Telegram channels associated with Flanders and the Netherlands. Building on previous observations concerning the propagation of disinformation on social media, the paper proposes a typology of the antagonistic discourse and narratives that circulate within these public channels. It thereby seeks to reconcile the comprehensive perspectives afforded by ‘big-data’ approaches with the analysis of Telegram in an event- and culture-specific context. Covering the period March 2017-July 2021, the paper specifically considers an inductively-collected dataset of 215 public Telegram channels and 371,951 messages pertaining to the relevant contexts, and bridges gaps between quantitative and qualitative methods by combining visual network analysis with discourse analysis. This combined approach reveals an expanding, highly diverse and dynamic network of Telegram channels, marked by overlapping antagonistic narratives, including traces of international conspiracy theories such as ‘The Great Reset’ and QAnon. These observations contribute to our understanding of how an emerging ‘alt-tech’ platform harbours and interconnects antagonistic actors and narratives in a specific linguistic and political context.
Originele taal-2English
Aantal pagina's19
TijdschriftFirst Monday : Peer-reviewed Journal on the Internet
Nummer van het tijdschrift9
StatusPublished - 6 sep 2022

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© This paper is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


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