Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/12374
DC FieldValueLanguage
dc.contributor.authorDjouvas, Constantinos-
dc.contributor.authorMendez, Fernando-
dc.contributor.authorTsapatsoulis, Nicolas-
dc.date.accessioned2018-07-25T10:29:30Z-
dc.date.available2018-07-25T10:29:30Z-
dc.date.issued2016-12-
dc.identifier.citationJournal of Innovation in Digital Ecosystems, 2016, vol. 3, no. 2, pp. 172-182en_US
dc.identifier.issn23526645-
dc.description.abstractFiltering data generated by so-called Voting Advice Applications (VAAs) in order to remove entries that exhibit unrealistic behavior (i.e., cannot correspond to a real political view) is of primary importance. If such entries are significantly present in VAA generated datasets, they can render conclusions drawn from VAA data analysis invalid. In this work we investigate approaches that can be used for automating the process of identifying entries that appear to be suspicious in terms of a users’ answer patterns. We utilize two unsupervised data mining techniques and compare their performance against a well established psychometric approach. Our results suggest that the performance of data mining approaches is comparable to those drawing on psychometric theory with a fraction of the complexity. More specifically, our simulations show that data mining techniques as well as psychometric approaches can be used to identify truly ‘rogue’ data (i.e., completely random data injected into the dataset under investigation). However, when analysing real datasets the performance of all approaches dropped considerably. This suggests that ‘suspect’ entries are neither random nor clustered. This finding poses some limitations on the use of unsupervised techniques, suggesting that the latter can only complement rather than substitute existing methods to identifying suspicious entries.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Innovation in Digital Ecosystemsen_US
dc.rights© Elsevieren_US
dc.subjectVoting advice applicationsen_US
dc.subjectData cleaningen_US
dc.subjectMachine learningen_US
dc.subjectData miningen_US
dc.subjectAnomaly detectionen_US
dc.subjectPsychometric Likert scaleen_US
dc.titleMining online political opinion surveys for suspect entries: An interdisciplinary comparisonen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationElectronic Democracy Centreen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.countrySwitzerlanden_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.jides.2016.11.003en_US
dc.relation.issue2en_US
dc.relation.volume3en_US
cut.common.academicyear2016-2017en_US
dc.identifier.spage172en_US
dc.identifier.epage182en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn2352-6645-
crisitem.journal.publisherElsevier-
crisitem.author.deptDepartment of Communication and Internet Studies-
crisitem.author.deptDepartment of Communication and Marketing-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.orcid0000-0003-1215-7294-
crisitem.author.orcid0000-0002-6739-8602-
crisitem.author.parentorgFaculty of Communication and Media Studies-
crisitem.author.parentorgFaculty of Communication and Media Studies-
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