China in the eyes of news media: a case study under COVID-19 epidemic
Bibliographic record
Abstract
As one of the early COVID-19 epidemic outbreak areas, China attracted the global news media’s attention at the beginning of 2020. During the epidemic period, Chinese people united and actively fought against the epidemic. However, in the eyes of the international public, the situation reported about China is not optimistic. To better understand how the international public portrays China, especially during the epidemic, we present a case study with big data technology. We aim to answer three questions: (1) What has the international media focused on during the COVID-19 epidemic period? (2) What is the media’s tone when they report China? (3) What is the media’s attitude when talking about China? In detail, we crawled more than 280 000 pieces of news from 57 mainstream media agencies in 22 countries and made some interesting observations. For example, international media paid more attention to Chinese livelihood during the COVID-19 epidemic period. In March and April, “progress of Chinese vaccines,” “specific drugs and treatments,” and “virus outbreak in U.S.” became the media’s most common topics. In terms of news attitude, Cuba, Malaysia, and Venezuela had a positive attitude toward China, while France, Canada, and the United Kingdom had a negative attitude. Our study can help understand China’s image in the eyes of the international media and provide a sound basis for image analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".