A Corpus-Based Critical Discourse Analysis of News Reports on the COVID-19 Pandemic in China and the UK
Bibliographic record
Abstract
Media, as important windows for the public to get to know timely information, play a vital role in influencing citizens’ attitudes as well as behaviors. From 2019, the Coronavirus Disease 2019 (COVID-19) pandemic, a global health emergency, has aroused great concern of the international community, including media. Varied in cultural context, political stand, and people’s ideology, however, media in different countries reported the COVID-19 dissimilarly. According to Fairclough’s critical discourse analysis (CDA) model, it is posited that the discrepancies in the reports of the COVID-19 can reflect ideological differences and have explanatory power in the development of the COVID-19 in distinct countries. Based on this premise, by utilizing the database analysis software AntConc 3.2.4w on self-built corpora, this study analyzed the news reports of different stages on the COVID-19 in China and the UK, i.e., in China Daily and The Guardian, respectively, and attempted to reveal the discourse characteristics in the two media, together with the discussion on their possible relations to the pandemic-controlling practices. The corpus-based analysis showed that China Daily used more objective and neutral words in the descriptions of the COVID-19 and expressed more active attitudes in fighting against the epidemic, whereas The Guardian used more negative words in describing the pandemic and words with weak restricting force when reporting policies concerning the control and prevention of the COVID-19 pandemic. Moreover, the comparison between the discourse before and after the lockdown demonstrated that the descriptions of the COVID-19 in the UK media transformed into a more objective and neutral one than before with an increased use of expressions of restriction and social conflicts. The same comparison in the discourse of China Daily found that words about sharing experience and promoting cooperation augmented noticeably. The above-mentioned findings were also discussed together with these two countries’ domestic epidemic situations and ideological differences, respectively.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".