Introduction
The study of online conspiracy theory communities presents unique methodological challenges. Online conspiracy theorists often adhere to an individualistic knowledge culture of “doing one’s own research” (Fenster 158). This results in a decentralised landscape of theories, narratives and communities that challenges conventional top-down approaches to analysis. Moreover, conspiracy theories tend to be discussed on the fringes of the online ecosystem, in chat groups, small subcultural Web forums and away from mainstream social media platforms such as Facebook and Twitter (Frenkel; see also De Zeeuw et al.). In this context, the messaging app Telegram has developed into a particularly prominent space (Rogers, “Deplatforming”; Urman and Katz). On the one hand, this platform is not quite part of the same mainstream as Facebook or Twitter, owing in part to its emphasis on security, “social privacy”, and lack of central moderation (Rogers, “Deplatforming”). But it is also not quite an “alternative social medium” (Gehl), as it does not position itself in opposition to mainstream platforms per se, nor does its business model centered around investor funding and advertisements present a break from the “dominant political economy” (ibid.). This ambiguous position might account for Telegram’s wide adoption, as well as its status as a relatively safe haven for communities deplatformed elsewhere – including a lively ecosystem of conspiracy theory communities (La Morgia et al.).
Because Telegram communities are distributed over a wide range of channels and chat groups, they cannot always be investigated using existing analytical approaches for social media research. Confronting this challenge, we propose and discuss a method for studying Telegram communities that repurposes the “methods of the medium” (Rogers, Digital Methods). Specifically, our method appropriates Telegram’s feature of forwarding messages from one group to another to discover interlinked distributed communities, collect data from these communities for close reading, and map their information sharing practices. 
In this article, we will first present this approach and illustrate the types of analyses the collected data might afford in relation to a brief case study on Dutch-speaking conspiracy theories. In this short illustration, we map the convergence of right-wing and conspiratorial communities, both structurally and discursively. As Vieten discusses, “digital pandemic populism during lockdown might have pushed further the mobilisation of the far right, also on the streets”. In the Dutch context there has been a demonstrated connection between the two. Because of this connection, we were drawn to the questions of what these entanglements might look like in a relatively unmoderated Telegram environment. We then proceed to discuss some strengths and limitations, identify avenues for future research, and conclude with some ethical, methodological and epistemological reflections.
Overview of Method
Our method first combines expert knowledge, and the affordances of the Telegram app’s ‘search’ function to retrieve a set of channels mentioning specific politicians or political parties, as well as other marked terms that might point towards far-right or conspiratorial content. This includes wakker (awakened), variations of batavier and geus (nationalist demonyms), names of known far right politicians (such as The Netherlands’ Thierry Baudet and Flanders’ Dries Van Langenhove) and conspiracy theory activists, and volk (a term meaning roughly “our people”). As this approach precludes discovery of related groups that do not match the queries exactly, this initial curated list is then supplemented with channels advertised elsewhere, such as those featured on the Websites of far-right politicians and organisations, as well as channels covered in mainstream news media.
This yields an initial expert list of channels, in our sample case of Dutch-speaking right-wing and conspiracist actors comprising 50 items. One might stop here, and collect data for this manually curated list of groups, as in Nikkhah et al.’s study of Telegram use among the Iranian diaspora in the United States, or Davey and Weinberg’s analysis of far-right groups used in the US military. But this would exclude any groups not known by the researchers; and groups are not always easy to naively discover on Telegram. Because of this, in a subsequent step, we expanded the initial set of relevant Telegram channels by crawling posts in these channels that were forwarded from other channels, constituting links between these channels. We used a custom crawler based on the open source library Selenium, which allows one to control a browser programmatically. The browser was then made to scroll through the Web-based view of the selected channels (e.g. https://t.me/s/durov). In principle, all messages ever posted in a channel are available in this view. We then follow those links, and store the names of the linked channels. Overall, this method thus presumes that if a channel forwards a message from another channel there will be some overlap in terms of topic of discussion between both, making the newly discover...

Telegram and Digital Methods: Mapping Networked Conspiracy Theories through Platform Affordances

10.5204/mcj.2878