The Government's Dividend: Complex Perceptions of Social Media Misinformation in China
Bibliographic record
Abstract
The social media environment in China has become the dominant source of information and news over the past decade. This news environment has naturally suffered from challenges related to mis- and dis-information, encumbered by an increasingly complex landscape of factors and players including social media services, fact-checkers, censorship policies, and astroturfing. Interviews with 44 Chinese WeChat users were conducted to understand how individuals perceive misinformation and how it impacts their news consumption practices. Overall, this work exposes the diverse attitudes and coping strategies that Chinese users employ in complex social media environments. Due to the complex nature of censorship in China and participants' lack of understanding of censor-ship, they expressed varied opinions about its influence on the credibility of online information sources. Further, although most participants claimed that their opinions would not be easily swayed by astroturfers, many admitted that they could not effectively distinguish astroturfers from ordinary Internet users. Participants' inability to make sense of comments found online lead many participants to hold pro-censorship attitudes: the Government's Dividend.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".