The Intermingling of State and Private Companies: Analysing Censorship of the 19th National Communist Party Congress on WeChat
Bibliographic record
Abstract
Abstract This paper examines the relationship between political events and information control on WeChat through a longitudinal analysis of keyword censorship related to China's 19th National Communist Party Congress (NCPC19). We use a novel method to track censorship on WeChat before, during and after the NCPC19 to probe the following questions. Does censorship change after an event is over? What roles do the government and private companies play in information control in China? Our findings show that the system of information control in China can trigger blunt reactions to political events. In addition to critical content around the Congress and leaders, WeChat also censored neutral and potentially positive references to government policies and ideological concepts. The decision making behind this censorship is a product of the interaction between the government, which influences actions through directives, and the companies, which ultimately implement controls on their platforms. While this system is effective in compelling companies to implement censorship, the intermingling of the state and private companies can lead to outcomes that may not align with government strategies. We call for a deeper understanding of the role of private companies in censorship and a more nuanced assessment of the government's capacity to control social media.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".