Evidence map›Paper›PMID 41143594›Full record

ArticleJournal of medical Internet research2025

Information Pathways and Voids in Critical German Online Communities During the COVID-19 Vaccination Discourse: Cross-Platform and Mixed Methods Analysis.

Silvan Wehrli, Anna-Maria Hartner, T Sonia Boender, Bert Arnrich, Christopher Irrgang

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Silvan WehrliCentre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Nordufer 20, Berlin, 13353, Germany, 49 030187543732.ORCID http://orcid.org/0009-0006-3377-1890
Anna-Maria HartnerCentre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Nordufer 20, Berlin, 13353, Germany, 49 030187543732.ORCID http://orcid.org/0000-0002-8502-9915
T Sonia BoenderDepartment of Infectious Diseases, Public Health Service of Amsterdam, Amsterdam, The Netherlands.ORCID http://orcid.org/0000-0002-4418-3713
Bert ArnrichDigital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.ORCID http://orcid.org/0000-0001-8380-7667
Christopher IrrgangCentre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Nordufer 20, Berlin, 13353, Germany, 49 030187543732.ORCID http://orcid.org/0000-0001-8274-1678

Funding

Gates Foundation INV-009125Wellcome Trust
6 · The paper itself

Abstract

Background: In Germany, the messaging app Telegram (Telegram FZ-LLC) served as a tool to organize protests against public health measures during the COVID-19 pandemic. A community of diverse groups formed around these protests, which used Telegram to discuss and share views outside of the general public discourse and mainstream information ecosystem. This increasingly included conspiracy theories and extremist content, propagated by sources that opposed the mainstream positions of the government and traditional media. While the use of such sources has been thoroughly investigated, the role of mainstream information in these communities remains largely unclear. Objective: We aimed to better understand the use of mainstream information, that is, from government actors and established media outlets, within critical Telegram communities in the context of the COVID-19 pandemic in Germany. We focused on the discourse about the COVID-19 vaccination, a key public health measure. As a central element of this study, we compared the Telegram discourse with the discourse on X (formerly Twitter, X Corp) and in online news-this cross-platform analysis aimed to put the results into a broader societal context. Methods: We analyzed Telegram, X, and news data between 2019 and 2023 for popular topics related to the COVID-19 vaccination discourse. We used a mixed methods approach, including text clustering for the exploration of popular topics, a 2-stage keyword filtering scheme for multitopic classification, link sharing analysis for assessing the prevalence of mainstream information, correlation-based time series analysis for measuring the similarity of discourse dynamics, and thematic analysis to examine the reasons for sharing information. Results: We identified 5 popular vaccination-related topics that were discussed on both Telegram and X, namely death, long COVID, measures in schools, mandatory vaccination, and virus variants. On average per topic, 58% (SD 5.2%) of Telegram posts and 21% (SD 4.9%) of X posts contained an external link. Of these posts containing external links, 11%-35% of Telegram posts and 44%-60% of X posts contained a mainstream link per topic. The correlations for week-to-week changes in discourse intensity between Telegram, X, and online mainstream news ranged from no positive association (coefficient <0.2) to strong positive relationships (coefficient >0.6) per topic. Finally, the thematic analysis resulted in 5 themes describing the usage patterns of mainstream information on Telegram and X. The identified themes are observing news, news comments, directed accusations, participation, and reference in discussion (only X). Conclusions: Mainstream information sources were part of the information mix within the analyzed critical Telegram communities. However, the role and prevalence of these sources varied. We argue that differences between platforms may indicate the existence of information voids, which pose a particular challenge in managing infodemics. These insights emphasize the importance of contextualized cross-platform analyses for understanding complex information pathways and their potential for targeted crisis communication.

Indexed as

COVID-19COVID-19 VaccinesSocial MediaVaccinationGermanyHumansPandemicsPublic HealthSARS-CoV-2COVID-19 VaccinesCOVID-19 vaccinationhealth informationinformation ecosystemmainstreamnewsquerdenkensocial mediatelegramtwitterX

Identifiers

PMID41143594
PMCPMC12557652

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.