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dc.contributor.authorSchunk, Adam
dc.date.accessioned2019-02-21 13:51:39 (GMT)
dc.date.available2019-02-21 13:51:39 (GMT)
dc.date.issued2019-02-21
dc.date.submitted2019-02-14
dc.identifier.urihttp://hdl.handle.net/10012/14476
dc.description.abstractOver the past years online social networks have become a major target for marketing strategies, generating a need for methods to efficiently spread information through these networks. Close knit communities have developed on these platforms through groups of users connecting with like minded individuals. In this thesis we use data pulled from Twitter's API and from simulations designed to mirror the Twitter network to pursue an in depth analysis of the network structure and influence of these communities. Through this analysis we draw several conclusions. First, the influence of users in these communities is correlated to the total number of followers in their neighborhood. Second, influential communities tend to be more tightly clustered than other areas of the network. Using these observations, we develop an algorithm to detect influential communities in Twitter and show that correctly prioritizing connections yields significant gains in message visibility.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectNetwork Analysisen
dc.subjectSocial Networken
dc.subjectTwitteren
dc.subjectSocial Influenceen
dc.titleAn Analysis on The Network Structure of Influential Communities in Twitteren
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws.contributor.advisorLarson, Kate
uws.contributor.advisorLarson, Kate
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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