Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/12972
DC FieldValueLanguage
dc.contributor.authorZenonos, Savvas-
dc.contributor.authorTsirtsis, Andreas-
dc.contributor.authorTsapatsoulis, Nicolas-
dc.date.accessioned2018-12-07T10:09:44Z-
dc.date.available2018-12-07T10:09:44Z-
dc.date.issued2018-08-
dc.identifier.citation16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, 2018, Athens, Greece, 12-15 Augusten_US
dc.identifier.urihttps://hdl.handle.net/20.500.14279/12972-
dc.description.abstractTwitter is one of the most popular social networking platforms that people use to communicate and interact. Organisations and companies use Twitter, as well as other social media platforms, for the marketing of their products or services. To achieve this goal they seek to partner with influential Twitter users, as a part of their influencer marketing strategy. Influencer marketing is considered more effective than traditional marketing. Influencers are more trustworthy than a business due to the fact that they have developed close connection with their followers. This marketing trend has played an important role in the rise of fake influencers in Twitter. Fake influencers inflate their follower counts by buying fake Twitter accounts from vendors and they manage to partner with companies. However, that partnership does not benefit companies as the influencer's engagement is fake. In this paper we analyse centrality and overall network characterization measures applied on Twitter fake influencer accounts and on legitimate influencer accounts. The results showed that the measures we propose are statistically significant and can be easily applied to automatically detect fake influencers on Twitter.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2018 IEEE.en_US
dc.subjectCentrality measuresen_US
dc.subjectCentralizationen_US
dc.subjectInfluencer marketingen_US
dc.subjectNetwork characterization measuresen_US
dc.subjectReciprocityen_US
dc.subjectTwitter fake influencersen_US
dc.titleTwitter influencers or cheated buyers?en_US
dc.typeConference Papersen_US
dc.doihttps://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00049en_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.countryCyprusen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
cut.common.academicyear2018-2019en_US
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.languageiso639-1en-
crisitem.author.deptDepartment of Communication and Marketing-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.orcid0000-0002-6739-8602-
crisitem.author.parentorgFaculty of Communication and Media Studies-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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