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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".