FAKE POPULARITY FOR REAL MONEY: COMMERCIAL ASTROTURFING AND DATA BUBBLE ON CHINESE DIGITAL PLATFORM
Bibliographic record
Abstract
This on-going research delineates the constructing of an interlocking ecosystem around popularity magnification on popular Chinese digital platforms, which we refer as “data bubble”. Similar to the bubble in a stock market or in real estate market in different economies where the price of assets substantially exceeds its intrinsic value, we propose “data bubble” as a neologism to describe the phenomenon and ecosystem of manipulating data to aim for an inflated popularity on Chinese digital platforms, which ultimately pitch to higher commercial and financial values. We argue that data bubble is laced with platform company’s commercial and financial imperatives, logics of datafication and popularity of platform as data infrastructure, and active participation from different user groups and complementors, and a deeply embedded mentality of “traffic is king”. It is achieved through mixed data practices including data optimization, commercial astroturfing, and counterfeit data manufacturing behind which a wide range of actors and entities are involved. They range from platforms, individual end users (fans in particular), influencers, multi-channel networks (MCNs) and incubators, celebrities and their agencies, click farms, and advertisers—all of them have achieved their own ends and thus actively participated in fabricating data bubble in one way or another. The practices of data manipulating and optimization by different participants in constructing data bubble, as a result, have driven the data metrics on Chinese platforms far over—and no longer representative of—the actual popularity.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".