Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28300
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dc.contributor.authorMavragani, Amaryllisen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.authorTsagarakis, Konstantinos Pen_UK
dc.date.accessioned2018-11-28T01:00:21Z-
dc.date.available2018-11-28T01:00:21Z-
dc.date.issued2018-11-30en_UK
dc.identifier.othere270en_UK
dc.identifier.urihttp://hdl.handle.net/1893/28300-
dc.description.abstractBackground: 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 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. Objective: This 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. Methods: Following 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. Results: All 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. Conclusions: The 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.en_UK
dc.language.isoenen_UK
dc.publisherJournal of Medical Internet Researchen_UK
dc.relationMavragani A, Ochoa G & Tsagarakis KP (2018) Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. JMIR, 20 (11), Art. No.: e270. https://doi.org/10.2196/jmir.9366en_UK
dc.rights©Amaryllis Mavragani, Gabriela Ochoa, Konstantinos P Tsagarakis. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.11.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectbig dataen_UK
dc.subjecthealth assessmenten_UK
dc.subjectinfodemiologyen_UK
dc.subjectGoogle Trendsen_UK
dc.subjectmedicineen_UK
dc.subjectreviewen_UK
dc.subjectstatistical analysisen_UK
dc.titleAssessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Reviewen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.2196/jmir.9366en_UK
dc.identifier.pmid30401664en_UK
dc.citation.jtitleJournal of Medical Internet Researchen_UK
dc.citation.issn1439-4456en_UK
dc.citation.volume20en_UK
dc.citation.issue11en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.date06/11/2018en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationDemocritus University of Thraceen_UK
dc.identifier.isiWOS:000450283200001en_UK
dc.identifier.scopusid2-s2.0-85056259214en_UK
dc.identifier.wtid1061593en_UK
dc.contributor.orcid0000-0001-6106-0873en_UK
dc.contributor.orcid0000-0001-7649-5669en_UK
dc.date.accepted2018-06-21en_UK
dcterms.dateAccepted2018-06-21en_UK
dc.date.filedepositdate2018-11-27en_UK
rioxxterms.apcpaiden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorMavragani, Amaryllis|0000-0001-6106-0873en_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.authorTsagarakis, Konstantinos P|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2018-11-27en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2018-11-27|en_UK
local.rioxx.filenamefc-xsltGalley-9366-217903-150-PB.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1439-4456en_UK
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