Ikpe Justice Akpan1, Obianuju Genevieve Aguolu2, Yawo Mamoua Kobara3, Rouzbeh Razavi4, Asuama A Akpan5, Murali Shanker4. 1. Department of Management & Information Systems, Kent State University, 330 University Drive Northeast, New Philadelphia, US. 2. Infectious Disease Internal Medicine Department, Yale School of Medicine, Yale University, New Haven, US. 3. Statistical and Actuarial Sciences, Western University, London, CA. 4. Department of Management & Information Systems, Kent State University, Kent, US. 5. Research and Development, Ibom International Center for Research and Scholarship, WINDSOR, CA.
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: To determine what people learned about the COVID-19 pandemic through "web search;" examine any association between what people learned about COVID-19 and behavior towards the public health guidelines; and analyze the impact of misinformation and conspiracy theories about the COVID-19 pandemic on people's behavior towards public health measures. METHODS: This paper undertakes an infodemiology study using the Google trends worldwide search index covering the first six months after the SARS-CoV-2 outbreak (January 1st, to June 30th, 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," "CommonCold," "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)] is 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, CommonCold, 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.
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: To determine what people learned about the COVID-19 pandemic through "web search;" examine any association between what people learned about COVID-19 and behavior towards the public health guidelines; and analyze the impact of misinformation and conspiracy theories about the COVID-19 pandemic on people's behavior towards public health measures. METHODS: This paper undertakes an infodemiology study using the Google trends worldwide search index covering the first six months after the SARS-CoV-2 outbreak (January 1st, to June 30th, 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," "CommonCold," "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)] is 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, CommonCold, 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.
Authors: Rachael Piltch-Loeb; Max Su; Brian Hughes; Marcia Testa; Beth Goldberg; Kurt Braddock; Cynthia Miller-Idriss; Vanessa Maturo; Elena Savoia Journal: JMIR Public Health Surveill Date: 2022-06-20
Authors: Pravesh Sharma; Anthony R Sinicrope; Pamela Sinicrope; Tabetha A Brockman; Nicole M Reinicke; Ian W West; Liana M Wiepert; Amy E Glasgow; Lindsey R Sangaralingham; Ashley L Holland; Christi A Patten Journal: JMIR Form Res Date: 2022-05-13