Google Trends in Infodemiology and Infoveillance: Methodology Framework

被引:270
作者
Mavragani, Amaryllis [1 ]
Ochoa, Gabriela [1 ]
机构
[1] Univ Stirling, Dept Comp Sci & Math, Fac Nat Sci, Univ Campus, Stirling FK9 4LA, Scotland
关键词
big data; health; infodemiology; infoveillance; internet behavior; Google Trends; HEALTH; INFORMATION; SUICIDE; TWITTER;
D O I
10.2196/13439
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
收藏
页码:59 / 77
页数:19
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