Abstract
Recent studies have shown that the stricter content moderation policies imposed by mainstream social networking sites (SNSs) stimulated the growth of low-moderated but relatively open discussion platforms such as Telegram. Despite Telegram’s growing popularity among (deplatformed) digital exiles, and high potential for news dissemination, information consumption, mobilization, and radicalization, little is known about information flows with respect to politically and socially relevant topics within the Telegramsphere. We scrutinize the Telegramsphere as an information-sharing ecosystem of current affairs by uncovering how information flows indicated by content-overlap and shared users influenced the structure of Telegram networks and shaped communities over time. Using state-of-the-art web-mining, neural topic modeling, and social network analysis techniques on a unique data set that spans the full messaging history (N = 2,033,661) of 174 Dutch-language public Telegram chats/channels, we show that over time, conspiracy-themed, far-right activist, and COVID-19-sceptical communities dominated the Dutch Telegramsphere of current affairs. Our findings raise concerns with respect to Telegram’s polarization and radicalization capacity in the context of consuming socially and politically relevant information online.
| Original language | English |
|---|---|
| Pages (from-to) | 3054-3078 |
| Number of pages | 25 |
| Journal | Information, Communication and Society |
| Volume | 26 |
| Issue number | 15 |
| Early online date | 16 Oct 2022 |
| DOIs | |
| Publication status | Published - 2023 |
Funding
This work is part of a project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 947695). This work made use of the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-2968.
| Funders | Funder number |
|---|---|
| SURF | EINF-2968 |
| Horizon 2020 Framework Programme | |
| European Research Council | |
| Horizon 2020 | 947695 |