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dc.contributor.authorThelwall, Mike
dc.date.accessioned2017-09-22T09:24:32Z
dc.date.available2017-09-22T09:24:32Z
dc.date.issued2017-09-21
dc.identifier.citationSocial media analytics for YouTube comments: potential and limitations 2017:1 International Journal of Social Research Methodologyen
dc.identifier.issn1364-5579en
dc.identifier.doi10.1080/13645579.2017.1381821
dc.identifier.urihttp://hdl.handle.net/2436/620685
dc.description.abstractThe need to elicit public opinion about predefined topics is widespread in the social sciences, government and business. Traditional survey-based methods are being partly replaced by social media data mining but their potential and limitations are poorly understood. This article investigates this issue by introducing and critically evaluating a systematic social media analytics strategy to gain insights about a topic from YouTube. The results of an investigation into sets of dance style videos show that it is possible to identify plausible patterns of subtopic difference, gender and sentiment. The analysis also points to the generic limitations of social media analytics that derive from their fundamentally exploratory multi-method nature.
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/13645579.2017.1381821en
dc.rightsArchived with thanks to International Journal of Social Research Methodologyen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectYouTubeen
dc.subjectSocial mediaen
dc.subjectsocial media analysis methodsen
dc.subjectbig dataen
dc.titleSocial media analytics for YouTube comments: potential and limitationsen
dc.typeJournal article
dc.identifier.journalInternational Journal of Social Research Methodologyen
dc.contributor.institutionSchool of Mathematics and Computing, University of Wolverhampton, Wolverhampton, UK
dc.date.accepted2017-09-14
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW220917MTen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0en
rioxxterms.licenseref.startdate2019-03-21en
dc.source.volume21
dc.source.issue3
dc.source.beginpage303
dc.source.endpage316
refterms.dateFCD2019-03-20T10:28:47Z
refterms.versionFCDAM
html.description.abstractThe need to elicit public opinion about predefined topics is widespread in the social sciences, government and business. Traditional survey-based methods are being partly replaced by social media data mining but their potential and limitations are poorly understood. This article investigates this issue by introducing and critically evaluating a systematic social media analytics strategy to gain insights about a topic from YouTube. The results of an investigation into sets of dance style videos show that it is possible to identify plausible patterns of subtopic difference, gender and sentiment. The analysis also points to the generic limitations of social media analytics that derive from their fundamentally exploratory multi-method nature.


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Archived with thanks to International Journal of Social Research Methodology
Except where otherwise noted, this item's license is described as Archived with thanks to International Journal of Social Research Methodology