Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/62035
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dc.contributor.authorAlMansour, Amal Abdullahen
dc.contributor.authorBrankovic, Ljiljanaen
dc.contributor.authorIliopoulos, Costas Sen
dc.date.accessioned2024-08-08T04:23:16Z-
dc.date.available2024-08-08T04:23:16Z-
dc.date.issued2014-
dc.identifier.citationi-KNOW '14: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business, p. 1-4en
dc.identifier.isbn9781450327695en
dc.identifier.urihttps://hdl.handle.net/1959.11/62035-
dc.description.abstract<p>Due to their openness and low publishing barrier nature, UserGenerated Content (UGC) platforms facilitate the creation of huge amounts of inaccurate content. Consequently, assessing UGC information credibility is developing into a vitally important research topic. This paper offers a systematic review of the current developments in assessing information credibility automatically in UGC platforms, focusing on microblogging service. It covers different aspects from dataset collection and feature usage, through classification techniques, to performance evaluation. A novel theoretical credibility model which integrates the evaluators' traits and context factors to assess information credibility is also presented along with important directions for future research on UGC information credibility.</p>en
dc.languageenen
dc.publisherAssociation for Computing Machineryen
dc.relation.ispartofi-KNOW '14: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Businessen
dc.relation.ispartofseriesACM International Conference Proceeding Seriesen
dc.titleEvaluation of Credibility Assessment for Microblogging – Models and Future Directionsen
dc.typeConference Publicationen
dc.relation.conferencei-KNOW 2014: 14th International Conference on Knowledge Technologies and Data-Driven Businessen
dc.identifier.doi10.1145/2637748.2638439en
local.contributor.firstnameAmal Abdullahen
local.contributor.firstnameLjiljanaen
local.contributor.firstnameCostas Sen
local.profile.schoolSchool of Science and Technologyen
local.profile.emaillbrankov@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference16th to 19th of September, 2014en
local.conference.placeGraz, Austriaen
local.publisher.placeUnited States of Americaen
local.identifier.runningnumber32en
local.format.startpage1en
local.format.endpage4en
local.peerreviewedYesen
local.contributor.lastnameAlMansouren
local.contributor.lastnameBrankovicen
local.contributor.lastnameIliopoulosen
dc.identifier.staffune-id:lbrankoven
local.profile.orcid0000-0002-5056-4627en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/62035en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleEvaluation of Credibility Assessment for Microblogging – Models and Future Directionsen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsi-KNOW 2014: 14th International Conference on Knowledge Technologies and Data-Driven Business, Graz, Austria, 16th to 19th of September, 2014en
local.search.authorAlMansour, Amal Abdullahen
local.search.authorBrankovic, Ljiljanaen
local.search.authorIliopoulos, Costas Sen
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2014en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/bdeae36a-4c49-4127-b26e-259874fd9fcfen
local.subject.for2020460199 Applied computing not elsewhere classifieden
local.subject.for2020460208 Natural language processingen
local.subject.seo2020220499 Information systems, technologies and services not elsewhere classifieden
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
Appears in Collections:Conference Publication
School of Science and Technology
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