What public and whose opinion? A study of Chinese online public opinion analysis
Bibliographic record
Abstract
As a recently emerged information service on the Chinese Internet, online public opinion analysis (网络舆情分析) is a type of service to monitor, assess, and synthesize online public communication through automated software and trained individuals. This article provides an industry overview of the rise of online public opinion in China and examines the political economy of the People’s Public Opinion Office. Second, this article looks at the marketing of the profession in news coverage, training materials, as well as published interviews with People’s Public Opinion Office’s managers and experts. Through a close reading of People’s Public Opinion Office’s published annual reports from 2007 to 2016 focusing on the issue of online rumor, this article delineates how the identification and prognostics of online rumor evolves with the State’s anti-rumor campaign that aims to forestall or pacify collective action. In sum, this article sets to examine the professionalization and industrialization of online public opinion analysis as a part of the co-evolutionary dynamics between the state and civil society in China. Using the government’s anti-rumor campaign as an example, this article critically investigates the mediating role online public opinion analysis plays in the “sausage factory” of public opinion gauging and monitoring, which sets the basis upon which policymaking process is guided and justified.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".