Corrupting the Cyber-Commons: Social Media as a Tool of Autocratic Stability
Bibliographic record
Abstract
Non-democratic regimes have increasingly moved beyond merely suppressing online discourse, and are shifting toward proactively subverting and co-opting social media for their own purposes. Namely, social media is increasingly being used to undermine the opposition, to shape the contours of public discussion, and to cheaply gather information about falsified public preferences. Social media is thus becoming not merely an obstacle to autocratic rule but another potential tool of regime durability. I lay out four mechanisms that link social media co-optation to autocratic resilience: 1) counter-mobilization, 2) discourse framing, 3) preference divulgence, and 4) elite coordination. I then detail the recent use of these tactics in mixed and autocratic regimes, with a particular focus on Russia, China, and the Middle East. This rapid evolution of government social media strategies has critical consequences for the future of electoral democracy and state-society relations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".