Investigating the Influence of Crisis Communication on Population Outrage amidst the Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has emphasized the importance of effective crisis communication. Specifically, varied regional crisis communication has caused a substantial impact on how communities perceive the virus and its risks. Our research analyzes the COVID-19 crisis communication responses seen in the United States, Canada, France, Spain, Croatia, Israel, Belgium, and the Netherlands. Several pandemic specific societal norms were analyzed for positive and negative trends to capture population outrage, based on the various crisis communication measures implemented. The norms focused on include political intervention, mask use, travel restrictions, e-commerce, vaccination rates, and demand for disinfectants. These norms were identified because of their considerable uptick in heightened awareness in the public eye in light of the pandemic. The trends seen in these pandemic specific norms affected each population uniquely due to varying strategies adopted for crisis communication. Differences in crisis communication methods can lead to distinctive responses in outrage caused by the pandemic. For example, the United States’ and the New Zealand’s populations starkly juxtapose one another’s crisis communication methods based on their outrage to pandemic specific societal norms. Our study investigates the influence that crisis communication has on the overall perception of the pandemic. Determining the influence that crisis communication has on population outrage can help improve future pandemic crisis communication to eventually bridge the gap between public outrage and the true risk at hand.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".