China’s Propaganda Strategy: Evidence from State-Owned News Networks’ English-Language Twitter Posts
Bibliographic record
Abstract
This paper seeks to analyze one element of China’s propaganda strategy. It seeks to find out whether one aspect of this strategy can be described as one of focusing on “positives about China” rather than “negatives about others”. This paper builds upon the research conducted by King et al. (2017), which found that the Chinese government employs a “cyber troop” of online influencers who post positive valence issues about the government in order to distract readers from negative valence topics surrounding the state. This study repeats King et. al’s study with Chinese government-owned English-language news outlets. Due to the different nature of the units of analysis (anonymous cyber troopers vs. official news agencies), a derivation of King et al.’s categorization scheme needed to be constructed which captures similar sentiments. Using this scheme, the paper analyzes the sentiment of the Twitter posts of these news outlets to determine whether the aforementioned strategy of “positives about China” rather than “negatives about others” is a genuine strategy of Chinese propaganda. Overall, there is evidence that the answer is affirmative. This could be driven by the Chinese government’s attempt to anchor public opinion favourably on issues of low public knowledgeability.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".