The Role of the Canadian Media During the Initial Response to the COVID-19 Pandemic: A Topic Modelling Approach Using Canadian Broadcasting Corporation News Articles
Bibliographic record
Abstract
BACKGROUND: Beginning as a local epidemic, COVID-19 has since rapidly evolved into a pandemic. As countries around the world battle this outbreak, mass media has played an active role in disseminating public health information. OBJECTIVE: The aim of this study was to get a better understanding of the role that the Canadian media played during the pandemic and to investigate the patterns of topics covered by media news reporting. METHODS: We used a data set consisting of news articles published on the Canadian Broadcasting Corporation (CBC) website between December 2019 and May 2020. We then used Python software to analyze the data using Latent Dirichlet Allocation topic modelling. Subsequently, we used the pyLDAvis tool to plot these topics on a 2D plane through multidimensional scaling and divided these topics into different themes. RESULTS: After removing articles that were published before the year 2019, we identified 6771 relevant news articles. According to the CV coherence value, we divided these articles into 15 topics, which were categorized into 6 themes. The three most popular themes were case reporting and testing (n=1738), Canadian response to the pandemic (n=1259), and changes to social life (n=1171), which accounted for 25.67%, 18.59%, and 17.29% of the total articles, respectively. CONCLUSIONS: Understanding the Canadian media's reporting on the COVID-19 pandemic shows that the Canadian pandemic response is a product of consistent government communication, as well as the public's understanding of and adherence to protocols.
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 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".