COVID‐19 Increases Online Searches for Emotional and Health‐Related Terms
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has powerfully shaped people's lives. The current work investigated the emotional and behavioral reactions people experience in response to COVID-19 through their internet searches. We hypothesised that when the prevalence rates of COVID-19 increase, people would experience more fear, which in turn would predict more searches for protective behaviors, health-related knowledge, and panic buying. METHODS: Prevalence rates of COVID-19 in the United States, the United Kingdom, Canada, and Australia were used as predictors. Fear-related emotions, protective behaviors, seeking health-related knowledge, and panic buying were measured using internet search volumes in Google Trends. RESULTS: We found that increased prevalence rates of COVID-19 were associated with more searches for protective behaviors, health knowledge, and panic buying. This pattern was consistent across four countries, the United States, the United Kingdom, Canada, and Australia. Fear-related emotions explained the associations between COVID-19 and the content of their internet searches. CONCLUSIONS: Findings suggest that exposure to COVID-19 prevalence and fear-related emotions may motivate people to search for relevant health-related information so as to protect themselves from the pandemic.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".