Collective pronouns, collective health actions: Predicting pandemic precautionary measures through online first-person plural pronoun usage across U.S. states

Mac Zewei Ma, Sylvia Xiaohua Chen and Xijing Wang

Social Science & Medicine, 2024, vol. 357, issue C

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.

Date: 2024
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DOI: 10.1016/j.socscimed.2024.117167

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