Pandemic Protesters on Telegram: How Platform Affordances and Information Ecosystems Shape Digital Counterpublics

Abstract

This study analyzes how platform affordances, their appropriation by movement actors, and these actors’ leveraging of information ecosystems—in combination—helped form a digital counterpublic during the COVID-19 pandemic. It draws on public communication data sent by more than 300 Telegram channels and group chats affiliated with the Querdenken movement over a 2-year period, and combines automated and manual text classification with network analysis. The study demonstrates how Telegram afforded connective and collective action in distinct ways that reflected the movement’s organizational structure and aims, as well as the impact of individual information-sharing on the process of movement-building itself. Accounting for time-dependent dynamics, the study also found that different parts of the counterpublic latched onto and sustained distinct information ecosystems to articulate their claims and mobilize contentious action.

Mapping a Dark Space: Challenges in Sampling and Classifying Non- Institutionalized Actors on Telegram

“…Both quantitative and computational content analyses have used link-based network sampling to collect a (large) complete population of actors belonging to or associating with a particular actor or movement (e.g., "COVID-19 protest groups on Telegram") using an automated snowball sampling approach starting with just one actor (Curley et al, 2022) or a long list of actors (Buehling & Heft, 2023;Zehring & Domahidi, 2023). For the 2019 protests in Hong Kong, Urman et al (2021) used the most prominent channel among Hong Kong activists, according to https://tgstat.com, as a starting seed.…”

“…Seed selection: Generally, as discussed in Urman and Katz (2022a), the seed sample has a disproportionately high impact on the overall sampling process compared with the nodes detected in later iterations. A diverse seed list (Schulze et al, 2022;Zehring & Domahidi, 2023;Buehling & Heft, 2023) can mitigate such biases. The underlying structures of the communication network and its clusters, which are to be uncovered via snowball sampling, are not necessarily dense and fully connected.…”

“…With such a regularly updated dataset, a stop condition could be defined by comparing various metrics (Münch et al, 2021) with the sampled network. Another method is network saturation, in which the search is terminated if only a few unknown channels are found in the last iteration (La Morgia et al, 2021;Buehling & Heft, 2023). 5) Content analysis is used to classify actors either identified a priori as relevant or col lected through content-or snowball-based procedures.…”