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

Steffen Springer1, Artur Strzelecki2, Michael Zieger1

  • 1SRH Wald-Klinikum Gera GmbH, Gera, Germany.

Healthcare Analytics (New York, N.Y.)
|March 20, 2023
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Summary

The coronavirus pandemic caused high global search interest. A new standard, Maximum Generable Interest (MGI), is proposed to objectively compare search volumes for health topics like COVID-19 and stroke.

Keywords:
Google TrendsInfodemiologyInfoveillanceMaximum generable interestSearch engine dataUniversal reference

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Area of Science:

  • Epidemiology
  • Public Health
  • Digital Health

Background:

  • The COVID-19 pandemic generated unprecedented global search interest, highlighting the need for standardized methods to analyze online search behavior.
  • Existing methods for comparing search interest lack universal standards, complicating the evaluation of research on health topics.
  • Search engine data, like Google Trends, offers valuable insights into public health concerns and information-seeking patterns.

Purpose of the Study:

  • To propose Maximum Generable Interest (MGI) as a universal reference standard for objectifying and comparing relative search interest.
  • To establish a framework for evaluating search volumes across different levels of interest, from high to low.
  • To demonstrate the utility of the MGI framework using stroke as a case study.

Main Methods:

  • Utilized Google Trends data to explore search interest related to the coronavirus pandemic.
  • Introduced the concept of Maximum Generable Interest (MGI) as a benchmark for search volume.
  • Developed a framework incorporating MGI and additional standards for medium and low search volumes.

Main Results:

  • The coronavirus pandemic demonstrated exceptionally high search interest, necessitating a standardized measurement approach.
  • Maximum Generable Interest (MGI) provides a universal reference for quantifying and comparing relative search interest.
  • The proposed framework, using MGI, enhances the comparability of research on search volumes for health topics.

Conclusions:

  • Maximum Generable Interest (MGI) offers a valuable tool for standardizing the analysis of online search data in health research.
  • This framework facilitates more objective comparisons of search interest, improving the evaluation of digital health trends.
  • The application to stroke illustrates the potential of MGI for diverse health-related topics.