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

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.

Notes

1. As an example, suppose that a respondent has four close alters. Two of the alters are perceived to avoid exposure to additional news stories about the issue of COVID-19 on mass media (i.e., the respondent selects “Yes” or “Probably Yes”), and two are perceived not to do so or as uncertain (the respondent selects “Certainly no” or “Not sure). Then, the scores on this measurement item about the avoidance of mass media COVID-19 information would be 2 + 0 = 2. Then, suppose that three of the alters are perceived to avoid exposure to additional news stories about the issue of COVID-19 on the Internet, and one is perceived not to do so or as uncertain. The scores on this measurement item about avoidance of Internet COVID-19 information would be 3 + 0 = 3. Then, we add these two scores up (2 + 3 = 5) to create a composite variable indicating the descriptive norm of media information avoidance.

Additional information

Funding

This work was supported by the University of North Carolina at Chapel Hill [UNC University Research Council (URC) 2018]; University of North Carolina at Chapel Hill [UNC University Research Council (URC) 2018].