JMIR Publications

ABSTRACT

Background:

The use of the Internet and web-based platforms to obtain public health information and manage health-related issues has become widespread in this digital age. The practice is so pervasive that the first reaction to obtaining health information is to 'google it.' As SARS-CoV-2 broke out in Wuhan, China, in December 2019, and quickly spread worldwide, people flocked to the Internet to learn about the novel coronavirus and the disease, COVID-19. Lagged response by governments and public health agencies to prioritize the Internet and the World Wide Web to disseminate information about the coronavirus outbreak and building trust gave room for others to quickly populate the social media, online blogs, news outlets, and websites with misinformation and conspiracy theories about the COVID-19 pandemic resulting in people’s deviant behaviors towards public health safety measures.

Objective:

This study investigates what people learned about COVID-19 through "web search," exposure to misinformation and conspiracy theories, and the impacts on behaviors towards public health safety measures.

Methods:

We used the Google trends worldwide search index covering the first six months after the SARS-CoV-2 outbreak (January to June 2020) when the public scrambled for information about the pandemic. Data analysis employed statistical trends, correlation and regression, principal component analysis, and predictive models.

Results:

(i). The principal components analysis identifies two latent variables comprising past coronavirus epidemics (pastCoVepidemics) and the ongoing COVID-19 pandemic (presCoVpandemic). Both principal components (PCs) were utilized significantly to learn about SARS-CoV-2 and COVID-19 and explained 88.78% variability. (ii). Three (3) PCs fuelled misinformation about COVID-19 [Misinformation(“Biological Weapon,” “VirusHoax,” “common cold,” “COVID-19Hoax,” “ChinaVirus”); ConspTheory1(“@5G”); ConspTheory1(“IngestBleach”)]. These PCs explained 84.85% of the variability, (iii). Two (2) PCs identified two components of public health measures [PubHealthMes1(“Social Distancing,” “WashHand,” “Isolation,” “Quarantine.”); PubHealthMes2(“WearMask”), which explained 84.7% of the variability. (iv). Based on the PCA results, log-linear, and predictive models [ConspTheory1(@5G)] are identified as a predictor of people’s behavior towards public health measures (PubHealthMes2). Although (r=0.83), (r=-0.11) for Misinformation(COVID-19Hoax, VirusHoax, common cold, and more) and ConspTheory2(Ingestbleach), respectively, with PubHealthMes1(social dist, handwash, isolation, and more), both were not statistically significant with (p=0.267), (p=0.13), respectively.

Conclusions:

Several studies focus on the impacts of social media and related platforms on spreading misinformation and conspiracy theories. This study provides the first empirical evidence to the mainly anecdotal discourse on the use of web search to learn about SARS-CoV-2 and COVID-19.