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

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Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: In 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 online data in public health issues, i.e., Infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and has been used in several topics up to this point, with health and medicine being the most focused-on subject. Online behavior is monitored and analyzed in order to examine the actual human behavior, so as to predict, better assess, and even prevent health related issues that constantly arise in everyday life. Objective: This systematic review aims at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends studies in health related topics from 2006 to 2016, so as to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Methods: Following the PRISMA guidelines for selecting the studies, the term ‘Google Trends’ was searched for 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 the ones that did not fall inside the topics of health and medicine or the selected article types. The published papers were then further categorized according to their methodological approach, namely visualization, seasonality, correlations, forecastings, and modeling. Results: All the examined papers consisted –by definition- of time series analysis, and all but two included data visualization. 23.08% studies used Google Trends data for examining seasonality, while correlations and modeling have been preformed in 39.42% and 32.69% of the studies, respectively. Only 8.65% of the studies have used Google Trends data for predictions and forecastings in health related topics, thus it is evident that a gap exists in forecasting using Google Trends data. Conclusions: The monitoring of online search queries can provide insight on 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.

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