“Anti-Regime Influentials” Across Platforms: A Case Study of the Free Navalny Protests in Russia
Bibliographic record
Abstract
The full-scale invasion of Ukraine by Russia in 2022 has put the future of the Russian opposition further at stake. The new limitations towards political, internet, and press freedoms have led to a severe disintegration of the anti-regime movement in Russia, including its leaders like Alexey Navalny. Digital platforms had previously hosted anti-Kremlin narratives online and played a role in the facilitation of Russian anti-regime protests. The latest scalable anti-regime rallies to date were the Free Navalny protests, caused by the imprisonment of Navalny in 2021. Digital platforms strengthened the voice of the Russian regime critics; however, their growing visibility online caused further suppression in the country. To understand this paradox, we ask which main anti-regime communicators were influential in the protests’ discussions on Twitter, YouTube, and Facebook, and how platform features have facilitated their influence during the Free Navalny protests. We develop a multi-platform methodological workflow comprising network analysis, social media analytics, and qualitative methods to map the Russian anti-regime publics and identify its opinion leaders. We also evaluate the cultures of use of platforms and their features by various Russian anti-regime communicators seeking high visibility online. We distinguish between contextual and feature cultures of platform use that potentially aid the popularity of such actors and propose to cautiously confer the mobilisation and democratisation potential to digital platforms under growing authoritarianism.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".