“Anti-Regime Influentials” Across Platforms: A Case Study of the Free Navalny Protests in Russia
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 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<em> </em>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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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