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Characterizing the Dynamics of Conspiracy Related German Telegram Conversations during COVID-19

Published: July 16, 2025 | arXiv ID: 2507.13398v1

By: Elisabeth Höldrich , Mathias Angermaier , Jana Lasser and more

Potential Business Impact:

Shows how fake news spreads on Telegram.

Business Areas:
Video Chat Information Technology, Internet Services, Messaging and Telecommunications

Conspiracy theories have long drawn public attention, but their explosive growth on platforms like Telegram during the COVID-19 pandemic raises pressing questions about their impact on societal trust, democracy, and public health. We provide a geographical, temporal and network analysis of the structure of of conspiracy-related German-language Telegram chats in a novel large-scale data set. We examine how information flows between regional user groups and influential broadcasting channels, revealing the interplay between decentralized discussions and content spread driven by a small number of key actors. Our findings reveal that conspiracy-related activity spikes during major COVID-19-related events, correlating with societal stressors and mirroring prior research on how crises amplify conspiratorial beliefs. By analysing the interplay between regional, national and transnational chats, we uncover how information flows from larger national or transnational discourse to localised, community-driven discussions. Furthermore, we find that the top 10% of chats account for 94% of all forwarded content, portraying the large influence of a few actors in disseminating information. However, these chats operate independently, with minimal interconnection between each other, primarily forwarding messages to low-traffic groups. Notably, 43% of links shared in the data set point to untrustworthy sources as identified by NewsGuard, a proportion far exceeding their share on other platforms and in other discourse contexts, underscoring the role of conspiracy-related discussions on Telegram as vector for the spread of misinformation.

Page Count
24 pages

Category
Computer Science:
Social and Information Networks