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Maximum generable interest: A universal standard for Google Trends search queries

The coronavirus or COVID-19 pandemic represents a health event with far-reaching global consequences, triggering a strong search interest in related topics on the Internet worldwide. The use of search engine data has become commonplace in research, but a universal standard for comparing different wo...

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Detalles Bibliográficos
Autores principales: Springer, Steffen, Strzelecki, Artur, Zieger, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997059/
https://www.ncbi.nlm.nih.gov/pubmed/36936703
http://dx.doi.org/10.1016/j.health.2023.100158
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author Springer, Steffen
Strzelecki, Artur
Zieger, Michael
author_facet Springer, Steffen
Strzelecki, Artur
Zieger, Michael
author_sort Springer, Steffen
collection PubMed
description The coronavirus or COVID-19 pandemic represents a health event with far-reaching global consequences, triggering a strong search interest in related topics on the Internet worldwide. The use of search engine data has become commonplace in research, but a universal standard for comparing different works is desirable to simplify the comparison. The coronavirus pandemic’s enormous impact and media coverage have triggered an exceptionally high search interest. Consequently, the maximum generable interest (MGI) on coronavirus is proposed as a universal reference for objectifying and comparing relative search interest in the future. This search interest can be explored with search engine data such as Google Trends data. Additional standards for medium and low search volumes can also be used to reflect the search interest of topics at different levels. Size standards, such as reference to MGI, may help make research more comparable and better evaluate relative search volumes. This study presents a framework for this purpose using the example of stroke.
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spelling pubmed-99970592023-03-09 Maximum generable interest: A universal standard for Google Trends search queries Springer, Steffen Strzelecki, Artur Zieger, Michael Healthc Anal (N Y) Article The coronavirus or COVID-19 pandemic represents a health event with far-reaching global consequences, triggering a strong search interest in related topics on the Internet worldwide. The use of search engine data has become commonplace in research, but a universal standard for comparing different works is desirable to simplify the comparison. The coronavirus pandemic’s enormous impact and media coverage have triggered an exceptionally high search interest. Consequently, the maximum generable interest (MGI) on coronavirus is proposed as a universal reference for objectifying and comparing relative search interest in the future. This search interest can be explored with search engine data such as Google Trends data. Additional standards for medium and low search volumes can also be used to reflect the search interest of topics at different levels. Size standards, such as reference to MGI, may help make research more comparable and better evaluate relative search volumes. This study presents a framework for this purpose using the example of stroke. The Author(s). Published by Elsevier Inc. 2023-11 2023-03-09 /pmc/articles/PMC9997059/ /pubmed/36936703 http://dx.doi.org/10.1016/j.health.2023.100158 Text en © 2023 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Springer, Steffen
Strzelecki, Artur
Zieger, Michael
Maximum generable interest: A universal standard for Google Trends search queries
title Maximum generable interest: A universal standard for Google Trends search queries
title_full Maximum generable interest: A universal standard for Google Trends search queries
title_fullStr Maximum generable interest: A universal standard for Google Trends search queries
title_full_unstemmed Maximum generable interest: A universal standard for Google Trends search queries
title_short Maximum generable interest: A universal standard for Google Trends search queries
title_sort maximum generable interest: a universal standard for google trends search queries
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997059/
https://www.ncbi.nlm.nih.gov/pubmed/36936703
http://dx.doi.org/10.1016/j.health.2023.100158
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