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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Inc.
2023
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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. |
format | Online Article Text |
id | pubmed-9997059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Author(s). Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
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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