Understanding the “infodemic”: social media news use, homogeneous online discussion, self-perceived media literacy and misperceptions about COVID-19

Author:

Su YanORCID,Borah PorismitaORCID,Xiao XizhuORCID

Abstract

PurposeThis study has three main purposes: (1) to investigate the association between social media news use and misperceptions about COVID-19; (2) to explore the mediating role of homogeneous online discussion; (3) and to understand whether the extent to which one perceives themselves as media-literate could moderate the relationship.Design/methodology/approachThe authors conducted an online survey and collected data through Amazon Mechanical Turk. A total of 797 participants aged 18 and above completed the survey. The average age of the respondents is 38.40 years (SD = 12.31), and 41.2% were female. In terms of party identification, 30.8% were reported leaning toward Republicans; 53.7% leaned toward Democrats, and 15.4% were reported neutral.FindingsResults from a moderated mediation model show that social media news use is positively associated with misperceptions about the COVID-19. Moreover, homogeneous online discussion was a significant mediator of the relationship between social media news use and misperceptions about COVID-19. Further, self-perceived media literacy (SPML) significantly moderated the main and indirect effects between social media news use and COVID-19 misperceptions, such that the associations became weaker among those with higher SPML.Originality/valueFindings provide insights into the significance of online information sources, discussion network heterogeneity and media literacy education. Although there have been many studies on misinformation, prior research has not examined these relationships, which may help provide solutions to cope with misinformation.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0305

Publisher

Emerald

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

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