Shaping Western Perceptions: The Role of English-language Verified Telegram Channels in Framing the Narratives Around the Russia-Ukraine War.
Bibliographic record
Abstract
In this study, we examine Telegram’s role as an information source on the Russia-Ukraine war, focusing broadly on how discussions on the platform may influence public opinion in the West. Specifically, we aimed to identify and study information-sharing practices of English-language verified Telegram channels, given their presumed audience in the West and their disproportionate impact on information-seeking and news consumption related to the war. Using a snowball sample of 21,907 Telegram channels, we identified 55 English-language verified channels linked to war-related content. Using a semi-automated topic analysis, we examined 125,840 public posts from these channels, all posted within the first year of the 2022 Russian invasion. While few channels exclusively focused on the war, our analysis found that far-right channels often echoed Kremlin-aligned narratives, aiming to discredit Western institutions and diminish support for Ukraine. We caution against relying on Telegram as a credible news source about the war, particularly for English-speaking users, due to the prevalence of low credibility and partisan content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".