Digital and mass media coverage of Russia's invasion of Ukraine compared: Uniformity within countries and diversity between countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article compares digital and mass media coverage of the first two years and five months of the Russo-Ukrainian War, from February 2022 to July 2024. The war is the first fully digital war as far as its informational dimension is concerned. The study aims to determine which type of digital war, participative or arrested, better describes the situation in the two belligerents. Particular attention is paid to Telegram, a messenger, because of its importance in covering the war. The analysis is comparative in several ways. In addition to comparing mass digital and mass media, it includes international comparisons of five countries: the two belligerents, the USA, the UK, and France. Two periods of the war are also compared. It is shown that similarities in how digital and mass media covered the war exceeded divergent patterns within countries, which indicates that uniformity prevailed over diversity in war coverage at the national level. National clusters of sources of political, media, and mass discourses emerged in comparisons between countries. An original design of computer-assisted content analysis was used to process a unique corpus of political, media, and mass discourses about the war. The corpus contained more than 273 million words in four languages: Ukrainian, Russian, English, and French.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it