Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29470
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dc.contributor.authorMavragani, Amaryllisen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.date.accessioned2019-05-11T00:01:42Z-
dc.date.available2019-05-11T00:01:42Z-
dc.date.issued2019-06en_UK
dc.identifier.othere13439en_UK
dc.identifier.urihttp://hdl.handle.net/1893/29470-
dc.description.abstractBackground: The use of Internet data is increasingly integrated in Health Informatics research and is becoming a useful tool in exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on what is trending and on the variations of the 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 towards health topics and in predicting diseases’ occurrence and outbreaks. Objective: 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, consisting of the main factors that need to be taken into account for a solid methodology base. Methods: We provide a step-by-step guide for the methodology that needs to be followed when researching with Google Trends; essential for robust results in this line of research. Results: At first, an overview of the tool and the data are presented, followed by the analysis of the key methodological points for ensuring the robustness of the results, i.e., selecting the appropriate keyword(s), region(s), period, and category. Conclusions: In the era of Big Data, the analysis of online queries is all the more integrated in health research. This article presents and analyzes the key points that need to be considered for a solid methodology basis when using Google Trends data, which is crucial for ensuring the value and validity of the results.en_UK
dc.language.isoenen_UK
dc.publisherJMIR Publications Inc.en_UK
dc.relationMavragani A & Ochoa G (2019) Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance, 5 (2), Art. No.: e13439. https://doi.org/10.2196/13439en_UK
dc.rights©Amaryllis Mavragani, Gabriela Ochoa. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 29.05.2019. 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 JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.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.subjectHealthen_UK
dc.subjectInfodemiologyen_UK
dc.subjectInfoveillanceen_UK
dc.subjectInternet Behavioren_UK
dc.subjectGoogle Trendsen_UK
dc.titleGoogle Trends in Infodemiology and Infoveillance: Methodology Frameworken_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.2196/13439en_UK
dc.identifier.pmid31144671en_UK
dc.citation.jtitleJMIR Public Health and Surveillanceen_UK
dc.citation.issn2369-2960en_UK
dc.citation.volume5en_UK
dc.citation.issue2en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailamaryllis.mavragani1@stir.ac.uken_UK
dc.citation.date23/03/2019en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.scopusid2-s2.0-85067403269en_UK
dc.identifier.wtid1278477en_UK
dc.contributor.orcid0000-0001-6106-0873en_UK
dc.contributor.orcid0000-0001-7649-5669en_UK
dc.date.accepted2019-03-23en_UK
dcterms.dateAccepted2019-03-23en_UK
dc.date.filedepositdate2019-05-10en_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.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2019-05-10en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2019-05-10|en_UK
local.rioxx.filenameb4b8275f-7851-45e0-a3b3-4b04b521a72c.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source2369-2960en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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