First Monday

Disinformation networks: A quali-quantitative investigation of antagonistic Dutch-speaking Telegram channels by Tom Willaert, Stijn Peeters, Jasmin Seijbel, and Nathalie Van Raemdonck

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 localised, 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, this 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.


Conclusions and future work




With permissive affordances including unmoderated chat groups and one-way messaging channels, Telegram has created new opportunities for the online circulation of antagonistic narratives (Urman and Katz, 2022; Walther and McCoy, 2021). This has made the platform a focal point for researchers of disinformation, who have empirically documented key dynamics governing its information-sharing networks and discourse.

Taking a comparative perspective, one line of inquiry has established that Telegram’s promise of lax moderation has attracted far-right influencers and other actors previously ‘deplatformed’ from mainstream social media such as Twitter (Rogers, 2020). This effectively recalls the position of so-called ‘alt-tech’ or ‘dark’ platforms including Gab or Parler (Cinque, et al., 2021), which gained notoriety as havens for free speech in the wake of Donald Trump’s 2021 ban from Twitter for “incitement of violence” (Twitter, 2021). As other platforms are thus called upon to moderate their users more strictly — and often do so — Telegram then has conversely become a place where fringe communities can thrive.

A second, related line of research has explored the convergence of discourse promoted by these actors, focusing specifically on the relation between far-right narratives and conspiracy theories. This approach acknowledges that, coinciding in large part with the coronavirus pandemic, Telegram established its user base at a time when social media discourse was marked by an intensified propagation of conspiracy narratives (Radicalisation Awareness Network [RAN], 2021). Extremist actors on the platform have accordingly been shown to co-opt conspiracy theories to advance their agendas (Gill, 2021), promoting Manichean narratives that split the world into “good and evil” or in-groups and out-groups [1], which as such can be characterised as politically extreme (Thomas, 2021). Following a process of narrative convergence also observed on other platforms (Tuters and Willaert, in press), these narratives might range from incorrectly blaming minorities for rising infection rates (Marone, 2022), to spuriously framing the pandemic as a government conspiracy aimed at diverting attention away from other issues such as mass migration (McNeil-Willson, 2020; Wondreys and Mudde, 2022).

Finally, a third line of inquiry has specifically set out to investigate instances of these dynamics in localised, language-specific contexts, mapping the scale, scope, and narrative trends marking Telegram communities from a European perspective. Examples include investigations of anti-Green Pass rhetoric in Italian Telegram chats by Spitale, et al. (2022), work by Becker, et al. (2021) on the Telegram groups of German far-right conspiracy theorist Attila Hildmann, research by Wich, et al. (2022) on hate-speech detection for German Telegram groups, and reporting on disinformation in German-language far-right and conspiracy groups by Smirnova, et al. (2022).

This paper contributes to the aforementioned body of work by offering an empirically informed analysis 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 in general, and Telegram in particular, this work proposes a typology of antagonistic discourse and narratives that circulate within these public channels. It thereby seeks to reconcile the comprehensive perspectives afforded by ‘big data’ approaches with an analysis of Telegram in an event– and culture–specific context. Covering the period March 2017–July 2021, this research specifically considers an inductively collected dataset of 215 public Telegram channels and 371,951 messages pertaining to relevant contexts, and bridges gaps between quantitative and qualitative methods by combining visual network analysis with discourse analysis.

Through this combined ‘quali-quantitative’ perspective (Venturini and Latour, 2010), we aim to capture aspects of a network of Telegram channels that has thus far attracted limited attention [2]. Previous ethnographic investigations have nevertheless highlighted that antagonistic groups from Flanders and the Netherlands rely heavily on social media and ‘alt-tech’ platforms for recruitment, mobilisation, and coordination of activities including online harassment and disinformation campaigns [3]. Throughout 2021, this became evident through a series of events and actions that were coordinated on Telegram and that subsequently trickled back into mainstream debates in both regions. In Flanders, for instance, Telegram harboured supporters of Jürgen Conings, a soldier with far-right sympathies who had gone missing in May 2021 after voicing his dissatisfaction with coronavirus mitigation measures, threatening Belgian virologist Marc Van Ranst, and stealing weapons from his barracks. The soldier’s disappearance sparked a weeks-long international search that attracted international media coverage (VRT, 2021; CNN, 2021), and public awareness of Telegram as an alternative messaging app for deplatformed actors was raised (Nieuwsblad, 2021).

In the Netherlands, Telegram made headlines when in October 2021 the Dutch Department of Justice blocked the public conspiracist Telegram channel ‘Bataafse Nieuws’, along with its associated chat group ‘De Bataafse Republiek’, with the aim of “end[ing] criminal offences or prevent[ing] new criminal offences” (Nederlandse Omroep Stichting [NOS], 2021). The channel and group, which had over 13,000 subscribers combined, had among other things posted threats against individuals, and propagated a conspiracist narrative that satanic, ritual child murders had been committed by prominent Dutch virologist Jaap van Dissel in the town of Bodegraven (NOS, 2021).

While these examples touch upon a wide range of societal and cultural issues, they also highlight two dynamics that are central to this paper. Firstly, Telegram groups such as the one in support of Conings point towards a confluence of conspiracist and far-right discourse. Such approximations between far-right groups or political parties and conspiracist movements in Flanders and the Netherlands have become a topic of intense debate. One example concerns the activities of Dutch politician Thierry Baudet, whose far-right party ‘Forum voor Democratie’ (FvD) maintains an active presence on Telegram, and who has been scrutinised for pushing what has been referred to as the ‘Cultural Marxism’ conspiracy theory (Breebaart, 2019). This raises the question to which extent far-right and conspiracist actors and discourse might be interwoven on Telegram.

Secondly, the conspiracy theories that figured in now-blocked channels such as ‘Bataafse Nieuws’ recall conspiratorial discourse that originated in the U.S., such as the QAnon conspiracy. This hints at international dynamics of disinformation propagation and adoption. Large-scale data analysis of Telegram channels has indeed established that QAnon has become a global phenomenon, and that its discourse has merged with discussion topics of interest to far-right communities, including “world politics, conspiracy theories, COVID-19 and the anti-vaccination movement” [4]. Previous research has furthermore suggested that online activities of Dutch fringe groups such as the ‘Studiegenootschap Erkenbrand’ were in part modelled after those of the so-called U.S. ‘alt-right’ [5], likewise embracing some of the conspiracy theories pushed by former U.S. president Donald Trump [6]. In Flanders, the Flemish far-right political party Vlaams Belang explicitly mentions Trump’s deplatforming as one of the main reasons for starting its own Telegram channel (, 2021). This in turn raises the question of how prominent international, ‘spreadable’ (that is: consciously shared and adaptable; Jenkins, et al., 2018) narratives are on Telegram channels associated with Dutch-speaking antagonistic communities. In other words, what can be made of conspiracy theories further ‘downstream’ from their original, often U.S.-centric, sources [7], and how might such narratives fit in a more general, empirically-informed typology of the discourse that circulates within this network of channels?





Data scraped from messaging platforms, such as Telegram, open up new perspectives for the empirical study of online disinformation. However, such ‘big data’ approaches come with the methodological challenges of extracting antagonistic narratives that can be wide-ranging and idiosyncratic, and, before that, of identifying relevant communities and messages in which those narratives might circulate. Arguably, both of these challenges call for bottom-up methods that, to an extent, let the data speak for themselves.

Starting from the question of how to identify relevant communities on Telegram from which texts might be collected, we follow a ‘digital methods’ approach that repurposes the methods of the medium (Rogers, 2013). Specifically, we re-use Telegram’s network-like affordances, which serve to produce loosely interconnected ‘spheres’ of like-minded groups and channels that form “an information-sharing ecosystem of current affairs” (Simon, et al., forthcoming). Studying the specific sphere or community of interest to us then requires us to first identify relevant channels, retrieve messages in order to subsequently describe the shared narratives that mark the communities under investigation. We focus our investigation on public Telegram channels, more specifically those channels that can be previewed from a browser. Our work can thus be considered to capture the most public and therefore immediately impactful aspect of the network we are interested in, as Telegram channels have been identified as a medium for amplifying and broadcasting messages, and for depositing resources (Van Raemdonck and Pierson, 2021). As extremism researcher Megan Squire (2020) has it, Telegram channels are thus geared towards public evangelising.

To sample relevant channels, we used a ‘snowballing’ method that has become well-established in Telegram research (Peeters and Willaert, 2022). This sampling method presumes that if a channel forwards a message from another channel, a meaningful relationship exists between both. At scale, mapping these relationships results in a network representation of connections between channels, revealing ‘hidden populations’ that might otherwise be hard to access (Johnson, 2014). We initiated this sampling process from a curated, expert list of approximately 50 relevant public Telegram channels. These were identified through an approach that reflects the various, often serendipitous, ways in which users might (directly or indirectly) encounter such channels. This included querying Telegram for channels with messages that contained keywords such as ‘QAnon’, ‘freedom’, ‘vaccination’, ‘awakened’, or ‘Flanders’; terms which might point towards far-right or conspiratorial content. Telegram channel links were likewise retrieved from Facebook communities that were found through similar keywords. For another, the seed list was further populated with Telegram channels associated with public figures or movements pertaining to the far-right or to communities that opposed coronavirus measures (for instance by organizing protests). This again comprised both searches on Telegram as well as the retrieval of channel links from public Web sites (such as those of political parties) and from antagonistic Facebook communities.

In compiling this initial seed list, we further maintained inclusive criteria, adhering to the working definition that conspiracy channels start from “the unnecessary assumption of a secret harmful plan when other explanations are more probable” [8], and that far-right channels support belief systems characterised by among others nationalism, racism, xenophobia, anti-democratic feelings, or strong state advocacy (Mudde, 1996).

In a next step, we deployed a purposely-built crawler to incrementally expand our list of potentially relevant public channels based on forwarded messages.The scraper used for the purposes of our investigation (see ‘Data availability statement’) was constructed around a Web driver ( software originally designed for automated Web site testing. We repurposed this software to simulate a user’s interaction with browser previews of public Telegram channels. To this end, our script automatically scrolled segments of these public Telegram channels into view. For each message in the visible segment, we then parsed the html code and stored a list of channel URLs from which messages had been forwarded. We started this process at the very first post in the channel and scrolled all the way down to the last available post at the moment of scraping (July 2021).

Our Web crawling efforts yielded an extended list of 215 public Telegram channels, from which we scraped all texts. This resulted in a dataset of 371,951 messages spanning the period March 2017–July 2021. The retrieved messages contained original contributions in Dutch, forwarded messages in English from international channels, as well as Dutch translations of content that originally appeared in English. This unique archive of Telegram data covered a period of substantial cultural, societal, and political turmoil in Flanders and the Netherlands, marked by among others the COVID-19 pandemic, violent street protests against the government’s response to the pandemic, the Dutch farmers’ protests (‘boerenprotest’), and the 2021 Dutch parliamentary elections.

These events took place against the backdrop of widely-covered international dynamics including the 2020 U.S. presidential elections, civil rights protests, and the Black Lives Matter movement, all of which contributed to and were closely entangled with the aforementioned antagonistic online environments. Consequently, our dataset contained traces of a wide range of topics and movements associated with the (international) ‘fringes’ of participatory Web culture. This is illustrated by an overview of the top 30 channels in our dataset by number of posts (Table 1), which included channel names evoking Donald Trump and the banned Reddit forum r/The_Donald; QAnon and other conspiracy theories, ‘patriotic resistance’, nationalism, and references to ‘redpilling’ or ‘awakening’. Somewhat unsurprising from the perspective of what Phillips and Milner refer to as the ‘downstream’ context of online information pollution [9], we thus found a number of channel names that evoked trends that originated in the context of U.S. politics.


Table 1: Thirty largest channels in dataset, by number of collected messages, annotated with their earliest activity on record. Channel names have been pseudonymised.
Channel nameSince (year)Number of messages


It should be mentioned here that we have chosen to pseudonymise the names of the channels discussed in this paper. While Telegram channels are public, and can be viewed by anyone from their browser (even when one does not have a Telegram account), one cannot simply assume that they are then openly available for analysis and further dissemination in a different (research) context. As Malin Sveningsson Elm writes, “even if users are aware of being observed by others, they do not consider the possibility that their actions and interactions may be documented and analysed in detail at a later occasion” [10]; public availability is insufficient as a heuristic for unaltered inclusion in publications on its own. Many (though not all) of the channels also have a relatively small number of subscribers, in any case fewer than for example the figure of 5,000 followers which Twitter has used in the past as an indication of an online account that can be considered a ‘public figure’ (see Kießling, et al., 2020). In the analysis on offer here, channel names have thus been replaced with fictional but broadly similar names, to offer some indication of the channel’s theme without unnecessarily exposing them to a wider audience. Only channels explicitly affiliated with political parties (‘FVDNL’ and ‘kiesdries’) and those already covered in mainstream news (the aforementioned ‘bataafsenieuws’ channel) are included with their original names.


Telegram can thus be seen as an interconnected network of channels and communities, a ‘Telegramsphere’ (Simon, et al., forthcoming) in which information can rapidly spread through the platform’s affordances. Analogously, the posts in our dataset can be conceived of as constituting a graph, with nodes representing channels and (directed) edges representing message-forwarding links. These graphs can be visualised and analysed using specialised software for network analysis, such as Gephi (Bastian, et al., 2009). Through a visual network analysis, one can then use the relative position and centrality of nodes and clusters of nodes to identify more closely related channels and communities of particularly strongly interlinked actors (Venturini, et al., 2015). For the purposes of the present investigation, we turn to network visualisations and analyses as heuristics for identifying channels of particular interest (e.g., based on the number of connections to other channels), and for situating the 215 retrieved channels within their wider (often international) contexts, thus supporting our interpretation of the channels’ discourses.

Regarding the latter, it can be assumed that the structural ties between channels reflect shared interests on the level of message contents. This implies that aggregated messages retrieved from the network as a whole make for a coherent dataset from which diverse narratives can be mined. The objective of our investigation is to build-up a typology of the narratives present in the dataset, which is illustrated and contextualised using expert knowledge. This typology was constructed following an inductive approach in which we jointly explored the dataset by means of the 4CAT analysis tool (Peeters and Hagen, 2021), iterating over posts and progressively fine-tuning the proposed narrative categories. The proposed typology thus comprises racist, antisemitic, and white victimhood narratives, anti-‘woke’ narratives, anti-establishment narratives, anti-government narratives, coronavirus conspiracies, QAnon conspiracy narratives, and ‘The Great Reset’ conspiracy theory.

In order to retrieve a sample of posts for each of the identified categories, seven specific queries were designed based on the communities’ specific vernaculars. To this end, we first tokenised all the posts in the dataset, retained tokens with over 1,000 occurrences. We then filtered these for noise and ambiguous terms, and assigned them as keywords to queries for each category (see Appendix). Querying our dataset again for messages matching these keywords allowed us to classify 33 percent (123,568) of our messages into one of seven thematic subsets. Given the nature of Telegram as a chat application, where many messages are very short and low on content or are non-textual media, this sample is thus nevertheless likely to capture a representative part of the substantial messaging in our dataset. These messages were then further contextualised and interpreted through close reading.




Network visualisations afford a bird’s-eye perspective of Telegram channels that is typically not accessible to users of the platform. Such an overview can contextualise a reading of the platform’s contents by shedding light on the overall dynamics and structure of the channel network. In this regard, our analysis reveals that the network of fringe channels on Telegram is highly dynamic. When we compared a snapshot of the network from June 2020 with that of July 2021, we observed a remarkable increase in the number of channels connected to the network.

In the former snapshot (Figure 1), the network comprised 334 nodes (channels). As will follow, the most salient of these channels were ‘content aggregators’ (‘2wakeup’, ‘thelatestclarion’, ‘NLCookery’) and channels that could be characterised as pertaining to ‘influencers’ such as ‘MaryFromMaastricht’. The channel of Flemish far-right politician Dries Van Langenhove was marked with a dark grey colour, and can be found in the periphery of the network. The July 2021 snapshot (Figure 2) comprised 4354 nodes (channels). This means that the network has become 13 times larger over the course of one year. Salient nodes in the network were the aforementioned channel named after ‘influencer’ ‘MaryFromMaastricht’, and an increased number of channels explicitly evoking the coronavirus pandemic (e.g., ‘anticovidhype’, ‘vaccines_sideeffects’). At this point in time, the channel of the Dutch political party ‘Forum voor Democratie’ (FvD) was also included in the network, where it was situated between the ‘right-wing’ bubble in which we find the channel by Flemish politician Dries Van Langenhove (‘kiesdries’), and the conspiratorial ‘core’ of the network.


Complete message forwards network in June 2020
Figure 1: Complete message forwards network in June 2020, ForceAtlas 2, node colours and sizes by degree. Channel names have been pseudonymised.


This observed expansion over time reflects Telegram’s overall rapid growth, which can at least in part be attributed to Telegram’s (perceived) role as a free haven for actors that were deplatformed from other social media, or that for political reasons present themselves as targets of deplatforming and censorship (Wijermars and Lokot, 2022). Urman and Katz (2022) indeed found that spikes of growth on the platform coincided with mass bans of the far-right actors on mainstream social media platforms around 2019. Another factor that might account for the observed growth of the network, is that the majority of antagonistic communities in our dataset, especially those opposing government measures, originated around the start of the COVID-19 pandemic in early 2020. As previous research by Zelenkauskaite, et al. (2021) on 4chan — another fringe platform — demonstrated, media events, which the COVID-19 pandemic could very much be counted as, are a time where there is an increase of hatred, specifically antisemetic sentiments. We will find that antisemitism was prevalent in our dataset and reoccured in all of the narratives we typologise in our discourse analysis.


Complete message forwards network in June 2021
Figure 2: Complete message forwards network in June 2021, ForceAtlas 2, node colours and sizes by degree. Channel names have been pseudonymised.


Figures 1 and 2, in which node sizes and colours are determined by node degree, illustrate that the retrieved network of Telegram channels is structurally diverse. While we can identify channels that contain few forwarded messages and that are thus only loosely associated with larger clusters or modules, it is also possible to discern a number of channels that we might label ‘content aggregators’ (Van Raemdonck and Pierson, forthcoming). These aggregators forward large numbers of messages from other channels and thus build up large repositories of heterogeneous content on a ‘pick and choose’ basis, often presenting themselves as nationalism-oriented ‘news’ channels, e.g., ‘bataafsenieuws’. This dynamic is not exclusive to our dataset, Baumgarter, et al.’s (2020) Pushshift Dataset seems to confirm the existence of such content aggregation channels all over the platform [11]. A similar function can be assigned to channels associated with Telegram ‘influencers’ or ‘microcelebrities’, such as the channel ‘MaryFromMaastricht’. While Telegram channels are usually unidirectional and generally do not allow for “parasocial” interactions between users, they can exert influence through content that is shared by other channels and in groups [12].


Complete message forwarding network, full dataset as scraped in July 2021
Figure 3: Complete message forwarding network (full dataset as scraped in July 2021), ForceAtlas 2, node colours and sizes by degree. Channels that were fully scraped and analysed further are marked with a dark grey colour. Channel names have been pseudonymised.


Figure 3 presents an overview of the full network, comprising 5,582 nodes. A purely visual assessment of this graph generates further hypotheses that frame our close readings of the channel contents. It appears for instance that the channel of ‘Forum voor Democratie’ has moved still closer to the conspiratorial core of the network — suggesting an approximation between right-wing political channels and channels propagating conspiracy theories. Arguably, antidemocratic narratives targeted at governments and ‘elites’, which have previously been identified by Mudde (1996) and later Carter (2005) as a key characteristic of far-right discourse, might thereby act as a bridge between coronavirus conspiracism and far-right discourse. In the remainder of this paper, we explore this dynamic and present our typology of narratives retrieved from the scraped channels, offering a more in-depth discussion of each of the seven narrative categories identified in the data and situating the latter in their culture- and event-specific contexts.

Coronavirus conspiracy narratives (67,056 messages)

The most prevalent narratives in our dataset were related to coronavirus conspiracy theories and criticism of the government’s response to the pandemic. The central story here was that the government suppressed individual freedoms in order to prime people for more extensive control, and that this was planned in advance. As previously indicated, Telegram channels were thereby staged as ‘news’ repositories that reinforce conspiratorial discourse by propagating unverified information, often from Anglophone sources. This included false claims that the virus was not real, that the reported number of deaths from the virus were exaggerated, or that the vaccine was created with the aim of depopulation. This finding squares with Gill’s (2021) observations of Australian conspiracist telegram channels in which COVID-19 was seen as an elite conspiracy and tool for social control. The discursive ties between Telegram’s network of Dutch-speaking fringe channels and international conspiratorial networks are well illustrated by the translated message below, which falsely claims that the vaccine spreads the virus [13]. The message thereby links to the Web site of NaturalNews, a well-known international disinformation network (Institute for Strategic Dialogue [ISD], 2020).

The vaccine is SPREADING covid! Just as we warned... it’s a global depopulation weapon deployed against humanity...

The vaccine IS the pandemic: 80% of nuns vaccinated at Kentucky convent tested positive for coronavirus two days later


A subset of these conspiratorial posts frames the pandemic as orchestrated by ‘globalists’ or Jewish elites, thus channelling the centuries-old antisemitic conspiracy theory of supposed Jewish world domination that also resurges in the QAnon and ‘The Great Reset’ conspiracy theories (Vrzal, 2020). Following Kofta, et al. (2020), such conspiracy theories serve as a universal explanation for crises in times of political uncontrollability, as was the case during the outbreak of the pandemic and in findings by Zelenkauskaite, et al. (2021) about media events on 4chan.

Anti-establishment sentiments inspired by the response to the pandemic also take on the form of calls for a reiteration of the Nürnberg trials, where politicians and virologists would be sentenced for their crimes against humanity. In the context of Flanders and the Netherlands, this became more concrete after the aforementioned Jürgen Conings threatened Belgium’s leading virologist Marc Van Ranst. Many channels in our dataset expressed support for the soldier’s mission. After the latter’s body was found weeks later, messages in Telegram channels cast doubt on the circumstances of his death, as noted in the example below. In more than one sense, then, messages concerning the case of Jürgen Conings are illustrative of the cross-pollination between far-right and conspiratorial narratives that mark our dataset.

Suddenly Jürgen was found dead today in the forest where they have been searching for weeks with hundreds of soldiers and police and dogs etc.... Does this sound logical ? NO!

What I think really happened.... 3 weeks ago they shot Jürgen because he knew way too much. Then they took his body and hid it for 3 weeks and today on Van Ranst’s birthday they dumped the body back. And immediately the media says Jürgen committed suicide with no further details on how or what? DOES NOT MAKE SENSE!!!! Rest in peace and I hope your death has not been in vain with many people waking up to the rotten and leftist politics we have in this country! A government full of losers!!!!

This business stinks, as always the media is lying about everything! All things on the Right are dangerous and extreme, everything Left is very good and peaceful....

Jürgen you are a HERO in many people’s eyes!!! ❤ RIP


QAnon conspiracy theory (9,197 messages)

Various Telegram channels in the dataset that adopted coronavirus conspiracy narratives also sympathised with the QAnon conspiracy theory. They used QAnon terminology such as ‘WWG1WGA’ (‘Where We Go One, We Go All’), and supported the false narrative that Donald Trump was secretly overthrowing a deep state, satanic, and pedophilic cabal. These channels mostly shared English messages or translations in Dutch, and some messages were forwarded from German QAnon channels. Of special interest here was the fact that like many conspiracy theories, the QAnon narrative was adapted to the local context, particularly the Dutch one. This corresponded with previous observations about the prevalence of the conspiracy theory in the Netherlands, which was found to rank among the top European countries where QAnon tweets originated (De Smedt and Rupar, 2020).

One such adaptation was centred around claims that Dutch head virologist Jaap Van Dissel had ritualistically abused people in the Dutch town of Bodegraven in the 1980s. Several QAnon-themed protests were coordinated on the channels to protest in the village’s cemetery. In July 2021, a Dutch judge ordered the original propagators of the conspiracy, the ‘Red Pill Journal’, to shut down their Telegram channel and remove all slanderous posts (Rechtbank Den Haag, 2021). Channels that similarly continued to orchestrate QAnon-inspired activities included the aforementioned ‘bataafsenieuws’, which was later blocked by the government (NOS, 2021). The message below illustrates how Telegram was used to appeal for concrete financial support for an eyewitness who fueled the Bodegraven conspiracy theory:

Emergency Appeal Joost Knevel

Support me in my fight against the worst evil in the world!!!

We have come a long way, many Cabal reptiles exposed!!!

Make sure I can continue to secure myself and my family!!!

And continue to investigate the many child murders!!!

Keep nothing to yourself people, go out and talk!!!

Also money is needed for technical gadgets!!! Account number: ######

Wwg1wga 😎


As follows from the example below, Telegram channels were operationalised to propagate the Bodegraven theory to the global QAnon community, for instance by translating posts into English and adding subtitles to videos:

Hi, another patriot from Holland here. Please please please make this one go viral!!! It’s about Joost Knevel who is one of the victims of sado-pedo abuse by using MK-Ultra in the West of Holland and witnessed the killing of a little girl by the head of the Dutch CDC!!! No nonsense all real. It’s the KEY to the fall of the cabal in Holland and other Royal countries!!! ;


In large part due to the Bodegraven case, we found that the majority of QAnon discourse in our dataset dated from the period May–June 2021. However, throughout the full collection of messages, we found allusions to the QAnon conspiracy theory, including messages blaming the political left for normalising pedophilia by teaching children about sex at an early age, and accusing the mainstream media of grooming children with (among others) childrens’ tv shows that normalised nudity. Some channels also targeted politicians and virologists as they were assumed to signal their pedophilic intentions through hidden messages and symbols.

‘The Great Reset’ conspiracy theory (4,023 messages)

In addition to QAnon, a smaller subset of messages in the dataset discussed and warned against ‘The Great Reset’. Based on an actual initiative of the World Economic Forum, ‘The Great Reset’ conspiracy theory falsely claims that the coronavirus pandemic was a hoax established by the WEF to install a New World Order government that would destroy capitalism, turning humans into communist worker drones. Most of the conspiracy was based on the phrase ‘you will own nothing and still be happy’, taken from the 2020 book tited COVID-19: The great reset written by WEF chairman Klaus Schwab, who is also at the core of the conspiracy theory. The alleged agenda of the World Economic Forum likewise goes against far-right and nationalist sentiments, as this message from a far-right Telegram channel in our dataset illustrates:

For many, the reason for participating in this COVID-19 project was fear. For those who refused to participate in this game, it was a taste of what the globalist order has in store for us.

[...] The reconstruction for after corona has been whispered by the WEF to “Build back better”. Klaus Schwab, the Jewish public face of the globalist order has said “You will own nothing and you will be happy.” The liberal-progressive elite see us as inferior and think it is okay to lie to us. We as nationalists must continue to fight against this world order. We must offer a better, healthy alternative to give our people the future they deserve.


Messages referring to ‘The Great Reset’ associate the conspiracy with the UN Agenda 2030 on Sustainable Development Goals, World Health Organization, and government leaders that have expressed some form of support for those institutions and agendas. Following previous observations, ‘The Great Reset’ conspiracy also incorporates the age-old conspiracy that Jewish elites run the world. There are several claims that ‘The Great Reset’ is run by Jews to oppress the ‘goyim’ (a term used in antisemitic discourse to refer to non-jewish people in a derogatory manner), as the ironic message below illustrates:

You will own nothing & be happy goy!


Racism and white victimhood (7,499 messages)

Racist narratives and discourse were found throughout our dataset. This included the aforementioned instances of antisemitism, as well as islamophobia and recurring racist slurs such as the ‘n-word’, derogatory uses of the word ‘Jew’, and dehumanizing terms such as ‘hybrid’ to refer to children of parents with mixed ethnic backgrounds.

One common racist narrative that bridges anti-‘woke’ discourse, anti-mainstream discourse, and coronavirus conspiracism, was that of ‘white victimhood’. This notion concerns whites casting themselves as victims of immigrant perpetrators or villains, pointing to perceived political correctness, positive discrimination, or welfare programs to construct the image of a system in which they and their culture are disadvantaged (King, 2015). Gill (2021) identified a similar victimhood narrative in Australian conspiracist Telegram channels, in which fascist groups position themselves as victims of, for example, political repression. An advanced version of this white victimhood is the ‘Great Replacement’ conspiracy theory, which states that white European populations are deliberately being replaced by minority communities through migration (Davey and Ebner, 2019).

On a local level, this narrative was evoked in Telegram posts complaining about the Dutch and Flemish tradition of ‘zwarte Piet’ (Black Pete) being ‘taken away’ by movements such as ‘Kick Out Zwarte Piet’ (Kick Out Black Pete). ‘Zwarte Piet’ is a contested blackface figure central to the popular Dutch and Flemish celebration of Sinterklaas (Saint Nicolas), which in recent years has stirred intense debate, also online (Keuchenius, et al., 2021). In this yearly celebration, the white bishop Sinterklaas visits the Netherlands (as well as Flanders) from Spain to give presents to well-behaved children, with the aid of his ‘black Petes’, who are imagined as Moorish servants whose silly, childlike behaviour starkly contrasts with that of the old white bishop (Wekker, 2016). An example of a message in which the narrative is prevalent is the following:

“We have started a campaign for Zwarte Piet, an age-old Dutch tradition that has been under attack from various anti-Dutch groups in the last few years. Zwarte Piet will always be welcome in the Netherlands!”


This message implicitly complains about racist discrimination against (white) Dutch and aims to delegitimised movements such as Kick Out Zwarte Piet or Black Lives Matter as being out to ‘end whiteness’. At the most concerning end of the spectrum, such complaints also take the form of calls to action and resistance, as illustrated below:


Voorpost is organising a manifestation at the Songfestival in Rotterdam on Saturday with the theme ‘Protect our families!’.
The Song Contest has degenerated into a left-liberal project that promotes gender madness and attacks traditional values. Making fun of cultures, barely singing in one’s own language, gender madness, immoral behaviour, drug use, anti-white racism, the list of anti-European hatred is getting longer and longer. The more people promote diversity, the less room they leave for differences ...

The manifestation of Voorpost will take place at 6 pm next to Ahoy. Several speakers will give a speech, including Florens van der Kooi, the national action leader of Voorpost.
The action has been registered with the municipality.

Outpost manifestation ‘protect our families!’
Start 6:00 PM
Location: next to Ahoy, see image
Sign up:


The Eurovision Song Contest was held in Rotterdam in 2021 (after being postponed the year before) and the ‘ethno-nationalist’ group Voorpost staged a demonstration to protest the contest. Again, ‘white victimhood’ was propagated as the singing contest supposedly ‘attacks traditional values’, ‘makes fun of (white European) culture’, and is overall racist against whites. As such, Voorpost called upon its Telegram followers to protest near the venue in which the contest was organised. Within this post racist and anti-‘woke’ discourse were combined, merging ‘great replacement conspiracy theory’ with ‘gender conspiracy theory’ (Marchlewska and Cichocka, 2020). In the latter it is believed that gender theory and gender studies represent an ideology that is pushed by a powerful and secret elite to trigger conflict between the sexes and subvert human nature.

Anti-‘woke’ narratives (10,428 messages)

We generally define anti-‘woke’ discourse as discourse that opposes emancipatory social movements, as exemplified by so-called ‘wokeness’ or ‘woke culture’. In general, the term ‘being woke’ refers to awareness about different forms of social inequality, for instance regarding gender or race (Whiteout, 2018). In our Telegram dataset, the terms ‘woke’ and ‘wokeness’ were often used pejoratively, and critiques of social inequalities were rendered suspicious. A common example of this was the narrative that the progressive agenda is a supposedly ‘Jewish’ agenda implemented to ‘fool the people’. Progressive movements such as Black Lives Matter, feminist movements, and LGBTQ+ activism are thereby supposedly orchestrated by a ‘Jewish elite’, tapping into a centuries old conspiracy of Jewish world domination. The following post illustrates this confluence of anti-‘woke’ and antisemitism, along with targeting mainstream institutions such as the World Economic Forum:

Satanic diversity program World economic forum #savethechildren

;️ 🌈 Transgender is not okay 🚫️ #protectthechilderen ✍ @dutchpatriot_channel ✊


We likewise found that such anti-h‘woke’ messages could contain explicit calls to action, and established links with wider international narratives such as the QAnon conspiracy theory:

🔔 The News but different

🔔 Take The Oath

🛑 STOP supporting these “Woke” companies with your hard earned money!

Support companies that share our values of God, country, and family! @PatrioticSwitch 🇺🇸

🗞 More:

🇳🇱 Join:

WWG1WGA Q Send (M)e

Join the fight


Anti-establishment narratives (45,483 messages)

A significant number of messages in the data promoted narratives that antagonised established institutions such as the media, universities, think tanks, and corporations. These suspicions manifested themselves also as a disdain for mainstream social media platforms such as Facebook or Twitter, which, as was already pointed out, were claimed to silence some of the actors and groups active on Telegram. By extension, traditional Dutch and Flemish news outlets were characterised as ‘lie machines’, the ‘lying press’ (a term recalling the Nazi slur ‘Lügenpresse’) or ‘fake news media’. Such narratives can again convergence with racist conspiracy theories, as illustrated by the following post about the murder of Sasha Johnson:

Sasha Johnson, a prominent figure in the Black Lives Matter movement, was shot in the head. However, that did not happen as the Flemish lying press told us. Help us with disproving the hatred of whites in the media. Go to


This post suggests that the press is propagating hatred against whites. Four men of colour are suspects of the shooting (at the time of writing, the case had not yet gone to trial). This was, according to the message, held silent by the press. A dominant narrative here is that when people with a migration background commit a crime, the media supposedly stay silent in an effort to promote a multicultural society, thus pushing an ‘elitist agenda’ or ‘woke culture’. Similar expressions of distrust for the media can be found in messages attacking the coronavirus mitigation measures, as illustrated below:

Fine planned for vaccine refusers? Number of vaccine refusers is apparently greater than presented in the mainstream.

Vaccinations are currently apparently failing more than the mainstream media makes out. Many people don’t show up for their second appointment, and the number varies from country to country. Officials are now working to fine the victims, reports.
Anyone who still trusts the statements of the mainstream media today must think that the “vaccination campaign” is in full swing. It is announced almost daily: this percentage has been vaccinated ... .


Anti-government narratives (53,443 messages)

Finally, reflecting wider international trends, a substantial number of messages expressed distrust of the Belgian and Dutch governments. A recurring theme here was that government measures against the coronavirus pandemic were exaggerated, unnecessary, or even unlawful. Throughout the dataset, we found examples of posts where antidemocratic narratives functioned as a bridge between coronavirus conspiracism and far-right discourse. As follows from the example below, which compares the former Dutch minister of health Hugo De Jonge to SS officer Josef Mengele, anti-government narratives can be marked by antisemitism and the downplaying of the Holocaust:

The News but different
🔔 Chat # whistleblowers for freedom

Dr. Hugo “VacciNazi” Mengele ... this person is an ordinary liar, thief and murderer. Co-responsible for the genocide on our people, our families, friends, brothers, and the vulnerable amongst us. When will this penny finally drop ladies and gentlemen ... .? 🙏❤️️🙏

🗞 More:

🇳🇱 Become a member:


Throughout the data, we likewise found many instances of Flemish politicians being called out explicitly. In the example below, an MP for the Green party was accused of hypocrisy and for holding minorities to a double standard. As in the previous example, this message explicitly propagates the aforementioned narrative of ‘white victimhood’:

Green MP An Moerenhout thinks Bart Somers is not left-wing enough. She believes that people who come together on the basis of ethnicity “strengthen society” and that they deserve subsidies. We all know, of course, that she only means the non-Flemish. Flemings who would like to set up an association for Flemish people would be discriminatory, racist and punishable. Moroccans who set up an association for Moroccans in Flanders should receive subsidies, according to Moerenhout. The hatred towards one’s own people is becoming more and more apparent.

Achmed 👦🏾: “I would like to found an association for ethnic Arabs.”

An Moerenhout on Twitter: “Yay! This makes our society stronger! You get subsidies Achmed!”

Jan 👦🏼: “I would like to set up an association for ethnic Flemish people.”

An Moerenhout on Twitter: “Racist! neo-Nazi! I will immediately call the police and UNIA! They had to put you in jail forever!”

Do you see the hypocrisy?




Conclusions and future work

This paper sought to make a contribution to the study of online disinformation by investigating the discourse of a network of far-right and conspiracy-oriented public Telegram channels related to Flanders and the Netherlands. These channels were identified by means of an inductive ‘snowballing’ method that expanded a seed list of channels based on message-forwarding links. The sampled channels were subsequently scraped in July 2021. This resulted in a graph-like dataset with interconnected channels, the oldest of which date back to 2017, marked by a rapid growth around the time of the coronavirus pandemic.

The retrieved dataset was analysed following a mixed-methods, ‘quali-quantitative’ approach, in which visual network analysis was used as a heuristic to identify influential (highly connected) channels, and to situate channels within their (international) contexts. Likewise, traces such as channel names or the presence of specific vernacular terms were used as guidelines to frame further analyses of the messages in the dataset. Following an inductively constructed typology of narratives, a sample of the messages was classified in one of seven categories: coronavirus conspiracy narratives, the QAnon conspiracy theory, ‘The Great Reset’ conspiracy theory, racism and white victimhood, anti-‘woke’ narratives, anti-establishment narratives, and anti-government narratives. In light of the study’s main research questions, it was thus established how different narratives cross-cut right-wing discourse and conspiracy theories, some of which echoing international narratives (e.g., QAnon). These observations thus contribute to our knowledge of how an emerging ‘alt-tech’ platform harbours and interconnects antagonistic actors and narratives in a specific linguistic and political context.

On a methodological level, this study aimed to balance the comprehensive perspectives afforded by ‘big data’ approaches with an analysis of Telegram in an event- and culture-specific context. Setting out from this initial exploration, future pathways for further research can be envisaged.

First, on the level of data collection, it should be acknowledged that our method of sourcing channel names through forwarded messages does not preclude the existence of isolated channels or clusters of channels that, for a lack of forwarded messages from channels that were already identified, eluded the scope of such snowball sampling efforts. For example, Telegram groups, which have been excluded from the study, may be a missing link in connecting certain clusters of channels, as these have previously been shown to be highly influential amplifiers of disinformation and hate speech (Pierson, 2021; also see Banaji, et al., 2019). As it is thus impossible to make any claims to completeness, we do put forward that our diverse and wide-ranging seed list allowed us to capture an indicative segment of this wider landscape. Likewise, our primary focus on far-right channels precluded a deeper inquiry into related left-wing movements and narratives. This might explain why most references to left-wing discourse that we did detect in the sphere under investigation were ironic or otherwise scornful representations.

Secondly, this study highlights the need for machine-guided methods for mining narratives from text, specifically from corpora marked by idiosyncratic vernaculars. While the present investigation relied on the construction of queries to extract narrative trends, future work might explore methods developed in the field of computational analysis of narratives, some of which has already started to explore conspiracy theories and other narratives associated with online disinformation (see Tangherlini, et al., 2020).

Finally, future elaborations of this study (including expansions of the dataset) might be conducted within a more general framework of ‘information warfare’, paying specific attention to how the communities under investigation have positioned themselves within on-going conflicts such as the war in Ukraine (see, for instance, Willaert, 2022, forthcoming). This might entail a broader inquiry into the actors (human or non-human) operating the channels under investigation (see Zelenkauskaite and Balduccini, 2017). End of article


About the authors

Tom Willaert is a postdoctoral researcher in digital methods at the Vrije Universiteit Brussel (VUB). His research aims to bridge methodological gaps between data science and humanities interpretative practice, with a focus on methods for the analysis of online (mis)information.
E-mail:tom [dot] willaert [at] vub [dot] be

Stijn Peeters is an assistant professor in the Department of Media Studies at the University of Amsterdam, and the Technical Director of the Digital Methods Initiative. His research focuses on the methodology of social media data capture, and the cross-platform spread of online (mis)information.
E-mail:s [dot] c [dot] j [dot] peeters [at] uva [dot] nl

Jasmin Seijbel is a Ph.D. candidate at the Erasmus School of History, Culture and Communication (ESHCC). Her doctoral research focuses on football-related antisemitic discourses(s) and how these intersect with other discriminatory discourse(s) both in on- and offine spaces. She also looks at educational programmes initiated to combat antisemitism in Dutch football.
E-mail:seijbel [at] eshcc [dot] eur [dot] nl

Nathalie Van Raemdonck is a Ph.D. candidate at the Vrije Universiteit Brussel (VUB). Her doctoral research focuses on platform affordances, social norms and the organic spread of misinformation and online radicalisation across platforms.
E-mail:nathalie [dot] van [dot] raemdonck [at] vub [dot] be


Data availability statement

The source code for the Telegram scraper discussed in this paper is hosted at



This project was funded by the European Commission under grant agreement INEA/CEF/ICT/A2020/2394296 (EDMO BELUX).



1. Barkun, 2013; Lee, 2020, p. 344.

2. But see Roks and Monshouwer (2020) and Blankers, et al. (2021) for criminological perspectives on Telegram activity related to the region. For an analysis of Dutch protest movements on Telegram, see Bakker, et al. (2021).

3. Ponsaers, 2020; Sterkenburg, 2021, pp. 206–207.

4. Hoseini, et al., 2021, p. 2.

5. Sterkenburg, 2021, p. 169.

6. Sterkenburg, 2021, p. 197.

7. Cf., Phillips and Milner, 2020, p. 5.

8. Aaronovitch, 2009, p. 5.

9. Phillips and Milner, 2021, p. 5.

10. Sveningsson Elm, 2009, p. 77.

11. Baumgarter, et al. (2020) found that the number of messages that were forwarded from users or channels was substantially higher (346,937 users or channels) than the number of channels where content was forwarded to (27,039).

12. Van Raemdonck and Pierson, 2021, pp. 2–3.

13. Throughout this section all examples were originally in Dutch but have been translated into English.



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Editorial history

Received 24 February 2022; revised 12 July 2022; accepted 17 August 2022.

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

Disinformation networks: A quali-quantitative investigation of antagonistic Dutch-speaking Telegram channels
by Tom Willaert, Stijn Peeters, Jasmin Seijbel, and Nathalie Van Raemdonck.
First Monday, Volume 27, Number 9 - 5 September 2022