Collective pronouns, collective health actions: Predicting pandemic precautionary measures through online first-person plural pronoun usage across U.S. states
Bibliographic record
Abstract
The COVID-19 pandemic has underscored the role of group identification in shaping collective health behaviors. Using the novel Pronoun-Influenced Collective Health Model - an integrated framework combining elements from health and social psychology theories - we investigated the relationship between online first-person plural pronoun usage and adherence to COVID-19 preventive measures across the United States. Analyzing weekly Google Trends data on English (Study 1) and Spanish (Study 2) first-person pronoun searches, alongside data on adherence to pandemic precautionary measures from early 2020 to late 2022, we found significant positive associations between relative first-person plural pronoun search volumes and adherence to social distancing, stay-at-home orders, vaccination rates, and proactive disease prevention information seeking. These associations remained robust after adjusting for potential confounding factors. A mini meta-analysis (Study 3) confirmed the consistency of our findings, revealing no significant moderation effects by language context or ecological-socio-cultural factors, suggesting broad generalizability. The implications of this research highlight the potential for tracking online collective language as a valuable indicator of and proxy for societal-level health engagement during crises. This novel digital linguistics approach, synergistically combining applied health and social psychology with big data from digital platforms such as Google, offers powerful tools for monitoring collective health actions across linguistic and cultural boundaries during large-scale health crises.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".