Google Trends in Infodemiology and Infoveillance: Methodology Framework

Abstract

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.

Keywords: Google Trends; big data; health; infodemiology; infoveillance; internet behavior.

Figure 1

Graphs of the variations in the online interest for the examined terms over the selected time frame in Google Trends.

Figure 2

Heat map for (a) “Asthma” in the United States from Jan 2004 to Dec 2014; (b) “Tuberculosis” in the United States and United Kingdom from March 24, 2007, to April 7, 2011; (c) “Chlamydia,” “Tuberculosis,” and “Syphilis” in Australia from Oct 5, 2012, to Dec 18, 2012; (d) “Asthma” in the United States, “AIDS” in the United Kingdom, and “Measles” in Canada from June 1, 2017, to July 15, 2018.

Figure 3

Google Trends’ (a) top related queries, (b) rising related topics, (c) top related topics, and (d) rising related queries for “Asthma” in the United States from Jan 1, 2004, to Dec 31, 2014.

Figure 4

Use of the “+” feature for including misspelled terms for (a) "Gonorrhea" compared to "Gonorrea"; (b) both terms by using the “+” feature.

Figure 5

Selection of the correct keyword for measles based on the use of accents in the respective translated terms in (a) Spanish, (b) Slovenian, (c) Swedish, and (d) Greek.

Figure 6

Differences in results with and without quotation marks for (a) “Breast Cancer” and (b) “HIV test.”.

Figure 7

Online interest in the term “Flu” over the past 5 years (a) worldwide and (b) in the United States.

Figure 8

Regional online interest in the term “Flu” at metropolitan level over the past 5 years in (a) California, (b) Texas, (c) New York, and (d) Florida.

Figure 9

Regional online interest in the term “Flu” at city level over the past 5 years in (a) Los Angeles, (b) Dallas, (c) New York, (d) Miami, (e) India, and (f) Greece.

Figure 10

Customized time range (a) from archive and (b) over the past week.