Examining factors affecting sharing of online information about Covid-19: an integrated model
Type of content
The plethora of unverified Covid-19 information circulating on the internet has catalysed into unwarranted health misinformation, disinformation, and rumours. Prior research has indicated that the spread of unverified information leads to misinformation (Huang et al., 2022). Health misinformation circulating online, particularly in a pandemic, develops into an infodemic and negatively affects individuals in countless ways (Tran et al., 2022). As prior research has recommended, the key to curbing the spread of health misinformation is to focus on individual information sharing drivers and to address those factors accordingly (Huang et al., 2022). The understanding of information sharing is currently fragmented, with research on information sharing emerging from different disciplines and theories. This research incorporates factors from different theories and collectively examines them in the context of unverified Covid-19 information sharing online.
Drawing on the Elaboration Likelihood Model (ELM), this study develops a theoretical model aimed at explaining unverified Covid-19 information sharing. Factors are carefully selected from existing literature, to provide a comprehensive model of unverified information sharing. These factors include information credibility, argument quality, information quality, source credibility, status seeking gratification, altruism, e-health literacy, information overload, and health beliefs (perceived susceptibility and perceived severity).
An online survey was conducted with 235 participants and data analysis was performed using SPSS software. Results revealed that status seeking gratification and information overload influenced unverified Covid-19 information sharing on online platforms. These findings indicate that the peripheral route (which requires less cognitive effort to comprehend information) has a stronger effect on predicating individual online information sharing behaviours than the central route (which requires more cognitive effort to comprehend information), suggesting that people are less likely to evaluate the information they find online, whether or not it is of good quality, before they share it. These results can inform policy makers in directing their efforts to develop and refine interventions that combat unverified information sharing.