Covid-19 and “Immune Boosting” on the Internet: A Content Analysis of Google Search Results
Bibliographic record
Abstract
Background: The spread of misinformation has accompanied the coronavirus pandemic, including topics such as immune boosting to prevent COVID-19. This study explores how immune boosting is portrayed on the Internet during the COVID-19 pandemic.Methods: We conducted a content analysis of 227 webpages compiled from Google searches in Canada and the US using the phrase “immune boosting” AND “coronavirus”. We coded webpages for typology, portrayal of immune boosting and supplements. We also recorded mentions of microbiome, whether the webpage was selling or advertising an immune boosting product or service, and any suggested strategies for boosting immunity. Results: No significant differences were found between webpages that appeared in the searches in Canada and the US. The most common types of webpages were from news sites (40.5%) and commercial sites (24.7%). The concept of immune boosting was portrayed as beneficial for avoiding COVID-19 in 85.5% of webpages and 40% of the webpages portrayed supplements as beneficial, but commercial sites were more likely to have these portrayals. The top immune boosting strategies were vitamin C (34.8%), diet (34.8%), sleep (34.4%), exercise (30.8%), and zinc (26.9%). Less than 10% of the webpages provide any critique of the concept of immune boosting.Interpretation: Pairing evidence-based advice for maintaining one’s health (e.g., healthy diet, exercise, sleep) with the phrase immune boosting and strategies lacking in evidence may inadvertently help to legitimize the concept, making it a powerful marketing tool. Results demonstrate how the spread of misinformation is complex and often more subtle than blatant fraudulent claims.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".