Maximum generable interest: A universal standard for Google Trends search queries.
Google Trends
Infodemiology
Infoveillance
Maximum generable interest
Search engine data
Universal reference
Journal
Healthcare analytics (New York, N.Y.)
ISSN: 2772-4425
Titre abrégé: Healthc Anal (N Y)
Pays: United States
ID NLM: 9918523186206676
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
28
01
2023
revised:
25
02
2023
accepted:
06
03
2023
entrez:
20
3
2023
pubmed:
21
3
2023
medline:
21
3
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36936703
doi: 10.1016/j.health.2023.100158
pii: S2772-4425(23)00025-4
pmc: PMC9997059
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100158Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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