|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Google Trends in Infodemiology and Infoveillance: Methodology Framework (Forthcoming/Available Online)|
|Citation:||Mavragani A & Ochoa G (2019) Google Trends in Infodemiology and Infoveillance: Methodology Framework (Forthcoming/Available Online). JMIR Public Health and Surveillance. https://doi.org/10.2196/13439|
|Abstract:||Background: The use of Internet data is increasingly integrated in Health Informatics research and is becoming a useful tool in exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on what is trending and on the variations of the 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 towards health topics and in predicting diseases’ occurrence and outbreaks. Objective: 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, consisting of the main factors that need to be taken into account for a solid methodology base. Methods: We provide a step-by-step guide for the methodology that needs to be followed when researching with Google Trends; essential for robust results in this line of research. Results: At first, an overview of the tool and the data are presented, followed by the analysis of the key methodological points for ensuring the robustness of the results, i.e., selecting the appropriate keyword(s), region(s), period, and category. Conclusions: In the era of Big Data, the analysis of online queries is all the more integrated in health research. This article presents and analyzes the key points that need to be considered for a solid methodology basis when using Google Trends data, which is crucial for ensuring the value and validity of the results.|
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|preprint-13439-accepted.pdf||Fulltext - Accepted Version||1.49 MB||Adobe PDF||View/Open|
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