Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Abstract

BackgroundIn the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life.ObjectiveThis systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling.ResultsAll the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data.ConclusionsThe monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

Google Trends: Opportunities and limitations in health and health policy research

“…35 One recent systematic review has identified forecasting as an area in particular need of development. 36 Data from Google Trends™ and other search engine repositories will never replace traditional data collection methods for population health. However, with further refinements and constructive dialogue between researchers and Google, search engine data can offer a powerful, real-time tool to assess how population health and health behaviours are changing within society.…”

Infodemiological Study Using Google Trends on Coronavirus Epidemic in Wuhan, China

“…Researchers are able to compare maximum 5 keywords using segments in one try. Studies on GT can be divided into four areas: infectious diseases; mental health, other diseases and general population behavior [11] and mainly are used to examine the seasonality [12].…”

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“…Researchers can compare a maximum of five keywords using segments in one try. Studies on GT can be divided into four areas-infectious diseases, mental health, other diseases, and general population behavior [45]-and are mainly conducted to examine seasonality [46].…”