Russian-Language Media of YouTube: Trends of the “Fifth Power” in 2021
Bibliographic record
Abstract
Purpose. Compared to 800 million users in 2012, global YouTube reached over 2 billion monthly active users in 2021. Just over a quarter of the world’s population visits YouTube every month. Worldwide, users watch over 1 billion hours of content every day. Russia is in the top five countries in 2021 in terms of the total estimated number of YouTube users – 58 million. According to Why Video, over 65 % of viewers perceive YouTube content as real life. Daily statistics show the scale of YouTube and it becomes clear that this is not just social media and video hosting, but a full-fledged “fifth power”. Results. Based on the analysis of 127 Russian-language media YouTube channels conducted in the fall-winter of 2021, as well as on expert interviews and monitoring of sociological research, the authors are trying to determine the vectors of development of the enormously popular platform. Conclusion. YouTube and audiovisual networks are becoming not only a means of procrastinating and entertaining viewers, but also an informational and educational source. Social media, and in particular YouTube, have established their own full-fledged media space with their own laws, trends, culture, fashion, etc. Most YouTubers create a completely competitive product without large-scale professional, especially television, production facilities, while their audience is many times greater than the television one. They re-invent journalistic genres that seem outdated on television and radio, raising hype about them. They earn money with the help of not only the YouTube platform but also advertising integrations. YouTubers grew into powerful media, developing a personal brand, choosing the most comfortable social media platforms for themselves, and successfully mastering new ones.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".