What Drives People Away from COVID-19 Information? Uncovering the Influences of Personal Networks on Information Avoidance

Yan Qu, Adam J. Saffer, Lucinda Austin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The pervasive of COVID-19 information has driven some to escape daily conversations or media coverage. A rich set of theoretical discussions and empirical studies help explain why individuals avoid health risk information, but few studies have explored social network antecedents to information avoidance. This study investigates how personal discussion networks about COVID-19 shape individuals' information avoidance behaviors. Using a nationally representative sample (N = 1,304), we examined the effects of network size, heterogeneity, ego-alter dissimilarity, and social norms. Our results suggest that the four network variables had varying effects on different forms of information avoidance. Notably, social norms significantly predicted individuals' information avoidance. The theoretical and methodological implications of our findings are discussed.

Original languageEnglish (US)
Pages (from-to)216-227
Number of pages12
JournalHealth communication
Volume38
Issue number2
DOIs
StatePublished - Feb 1 2023

PubMed: MeSH publication types

  • Journal Article

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