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A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy

The scientific community has classified COVID-19 as the worst pandemic in human history. The damage caused by the new disease was direct (e.g., deaths) and indirect (e.g., closure of economic activities). Within the latter category, we find infodemic phenomena such as the adoption of generic and sti...

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Autores principales: Rovetta, Alessandro, Castaldo, Lucia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8801192/
https://www.ncbi.nlm.nih.gov/pubmed/35094682
http://dx.doi.org/10.1186/s12874-022-01523-x
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author Rovetta, Alessandro
Castaldo, Lucia
author_facet Rovetta, Alessandro
Castaldo, Lucia
author_sort Rovetta, Alessandro
collection PubMed
description The scientific community has classified COVID-19 as the worst pandemic in human history. The damage caused by the new disease was direct (e.g., deaths) and indirect (e.g., closure of economic activities). Within the latter category, we find infodemic phenomena such as the adoption of generic and stigmatizing names used to identify COVID-19 and the related novel coronavirus 2019 variants. These monikers have fostered the spread of health disinformation and misinformation and fomented racism and segregation towards the Chinese population. In this regard, we present a comprehensive infodemiological picture of Italy from the epidemic outbreak in December 2019 until September 2021. In particular, we propose a new procedure to examine in detail the web interest of users in scientific and infodemic monikers linked to the identification of COVID-19. To do this, we exploited the online tool Google Trends. Our findings reveal the widespread use of multiple COVID-19-related names not considered in the previous literature, as well as a persistent trend in the adoption of stigmatizing and generic terms. Inappropriate names for cataloging novel coronavirus 2019 variants of concern have even been adopted by national health agencies. Furthermore, we also showed that early denominations influenced user behavior for a long time and were difficult to replace. For these reasons, we suggest that the assignments of scientific names to new diseases are more timely and advise against mass media and international health authorities using terms linked to the geographical origin of the novel coronavirus 2019 variants. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01523-x.
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spelling pubmed-88011922022-01-31 A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy Rovetta, Alessandro Castaldo, Lucia BMC Med Res Methodol Research The scientific community has classified COVID-19 as the worst pandemic in human history. The damage caused by the new disease was direct (e.g., deaths) and indirect (e.g., closure of economic activities). Within the latter category, we find infodemic phenomena such as the adoption of generic and stigmatizing names used to identify COVID-19 and the related novel coronavirus 2019 variants. These monikers have fostered the spread of health disinformation and misinformation and fomented racism and segregation towards the Chinese population. In this regard, we present a comprehensive infodemiological picture of Italy from the epidemic outbreak in December 2019 until September 2021. In particular, we propose a new procedure to examine in detail the web interest of users in scientific and infodemic monikers linked to the identification of COVID-19. To do this, we exploited the online tool Google Trends. Our findings reveal the widespread use of multiple COVID-19-related names not considered in the previous literature, as well as a persistent trend in the adoption of stigmatizing and generic terms. Inappropriate names for cataloging novel coronavirus 2019 variants of concern have even been adopted by national health agencies. Furthermore, we also showed that early denominations influenced user behavior for a long time and were difficult to replace. For these reasons, we suggest that the assignments of scientific names to new diseases are more timely and advise against mass media and international health authorities using terms linked to the geographical origin of the novel coronavirus 2019 variants. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01523-x. BioMed Central 2022-01-30 /pmc/articles/PMC8801192/ /pubmed/35094682 http://dx.doi.org/10.1186/s12874-022-01523-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Rovetta, Alessandro
Castaldo, Lucia
A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy
title A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy
title_full A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy
title_fullStr A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy
title_full_unstemmed A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy
title_short A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy
title_sort new infodemiological approach through google trends: longitudinal analysis of covid-19 scientific and infodemic names in italy
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8801192/
https://www.ncbi.nlm.nih.gov/pubmed/35094682
http://dx.doi.org/10.1186/s12874-022-01523-x
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