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Google Trends in Infodemiology and Infoveillance: Methodology Framework.
Mavragani, Amaryllis; Ochoa, Gabriela.
Afiliación
  • Mavragani A; Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom.
  • Ochoa G; Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom.
JMIR Public Health Surveill ; 5(2): e13439, 2019 May 29.
Article en En | MEDLINE | ID: mdl-31144671
Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JMIR Public Health Surveill Año: 2019 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JMIR Public Health Surveill Año: 2019 Tipo del documento: Article País de afiliación: Reino Unido