What they do in the shadows: examining the far-right networks on Telegram

ABSTRACT

The present paper contributes to the research on the activities of far-right actors on social media by examining the interconnections between far-right actors and groups on Telegram platform using network analysis. The far-right network observed on Telegram is highly decentralized, similarly to the far-right networks found on other social media platforms. The network is divided mostly along the ideological and national lines, with the communities related to 4chan imageboard and Donald Trump’s supporters being the most influential. The analysis of the network evolution shows that the start of its explosive growth coincides in time with the mass bans of the far-right actors on mainstream social media platforms. The observed patterns of network evolution suggest that the simultaneous migration of these actors to Telegram has allowed them to swiftly recreate their connections and gain prominence in the network thus casting doubt on the effectiveness of deplatforming for curbing the influence of far-right and other extremist actors.

KEYWORDS:

• Far right
• social media
• network analysis
• Telegram

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 Following Mudde (2007, 2019), in this paper we use “far right” as umbrella term that includes different subgroups such as extreme right and radical right.

2 All the data collected is publicly available to any Telegram user, and, for ethical reasons, in the course of the analysis we relied only on aggregated data without attributing any messages to individual users. Private channels when mentioned are represented with hashes only so one cannot access them directly.

3 No content collected for private groups/channels.

4 See supplementary material for the corresponding data.

5 See supplementary material for the corresponding data.

Additional information

Notes on contributors

Aleksandra Urman

Aleksandra Urman is a postdoctoral researcher at the University of Bern. Her PhD dissertation defended in May 2020 examines polarization on social media from a comparative perspective. Aleksandra’s research interests include online political communication, algorithmic biases, and computational research methods. [email: [email protected]]

Stefan Katz

Stefan Katz is a researcher at the Business School of the Bern University of Applied Sciences. He holds a Master’s degree in System Dynamics from the University of Bergen. Stefan’s research interests include network analysis and the combination of machine learning and simulation methodologies.