About: Coronavirus-related online web search desire amidst the rising novel coronavirus incidence in Ethiopia: Google Trends-based infodemiology

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About: Coronavirus-related online web search desire amidst the rising novel coronavirus incidence in Ethiopia: Google Trends-based infodemiology Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Coronavirus-related online web search desire amidst the rising novel coronavirus incidence in Ethiopia: Google Trends-based infodemiology
Creator	Awoke, Mengist Rajan, Asha Rovetta², Alessandro Terefe, Behailu Terefe¹, Behailu
source	MedRxiv
abstract	Abstract Background: During outbreaks, like novel coronavirus (COVID 19) disease pandemic, social communication and behaviors are very important. Under such circumstances, individual activities in social media and other online platforms will increase tremendously resulting in the circulation of information in the community. These pieces of information can be infodemic monikers misleading the community. Thus exploring the trends of public interest in searching COVID 19 related issues and checking information circulating in the public is highly crucial. Objective: This study aimed to explore users' concerns towards coronavirus related online web search and to investigate the extent of infodemic monikers (misinformation) adopted for identifying the virus in the early stage of COVID 19 spread in Ethiopia. Methods: Google Trends was employed in exploring the tendency towards coronavirus related web search activities in Ethiopia from March 13 to May 8, 2020. Keywords of the different names of COVID 19 and health related issues were used to investigate the trends of public interest in searching from Google over time. Relative search volume (RSV), Average peak comparison (APC), and associated graphs were used to compare the trends of online search interests. Pearson correlation coefficient was calculated to check for the presence of correlation. Result: During the study period, the top names users adopted to identify the virus in Ethiopia were corona, virus, coronavirus, corona virus, China coronavirus, and COVID 19. In almost all search activities, the users employed infodemic monikers to identify the virus (overall relative search percentage of 99%). Updates related issues (APC=60, 95% CI, 55, 66) were the most commonly trending health related searches on Google followed by mortality (APC=27, 95% CI, 24, 30) and symptoms (APC=55, 95% CI, 50, 60) related issues. The regional comparison showed the highest cumulative peak for the Oromia region (cumulative total RSVs, 536) followed by the Tigray region (cumulative total RSVs, 512) on querying health related information from Google. No significant correlation was detected between COVID 19 cases per region/city and the maximum peaks of health related searches. Conclusion: This study revealed an initial increase in the public interest of COVID 19 related Google search, but this interest was declined over time. Tremendous circulation of infodemic monikers for the identification of the virus was also noticed in the country. The authors recommend for the government and other stakeholders to work immensely to keep the people alert on coronavirus-related issues and to promote the official names of the virus, like COVID 19, SARS CoV 2 in the public to decrease the circulation of misleading and misinformation amid the outbreak. Keywords: Coronavirus, infodemiology, infodemic monikers; Google Trends, Ethiopia
has issue date	2020-07-25(xsd:dateTime)
bibo:doi	10.1101/2020.07.23.20158592
has license	medrxiv
sha1sum (hex)	a2cae3244c27af4874895a9e05f148953cd3257c
schema:url	https://doi.org/10.1101/2020.07.23.20158592
resource representing a document's title	Coronavirus-related online web search desire amidst the rising novel coronavirus incidence in Ethiopia: Google Trends-based infodemiology
resource representing a document's body	covid:a2cae3244c27af4874895a9e05f148953cd3257c#body_text
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