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dc.contributor.authorEzpeleta Gallastegi, Enaitz
dc.contributor.authorIturbe Urretxa, Mikel
dc.contributor.authorGaritano Garitano, Iñaki
dc.contributor.authorVelez de Mendizabal Gonzalez, Iñaki
dc.contributor.authorZurutuza Ortega, Urko
dc.date.accessioned2019-04-03T10:44:28Z
dc.date.available2019-04-03T10:44:28Z
dc.date.issued2018
dc.identifier.isbn978-3-319-92639-1 onlineen
dc.identifier.isbn978-3-319-92638-4 printen
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=148654en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/1178
dc.description.abstractIn the same manner that Online Social Networks (OSN) usage increases, non-legitimate campaigns over these types of web services are growing. This is the reason why signi cant number of users are affected by social spam every day and therefore, their privacy is threatened. To deal with this issue in this study we focus on mood analysis, among all content-based analysis techniques. We demonstrate that using this technique social spam filtering results are improved. First, the best spam filtering classifiers are identified using a labeled dataset consisting of Youtube comments, including spam. Then, a new dataset is created adding the mood feature to each comment, and the best classifiers are applied to it. A comparison between obtained results with and without mood information shows that this feature can help to improve social spam filtering results: the best accuracy is improved in two different datasets, and the number of false positives is reduced 13.76% and 11.41% on average. Moreover, the results are validated carrying out the same experiment but using a different dataset.en
dc.description.sponsorshipGobierno de Españaes
dc.description.sponsorshipGobierno de Españaes
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherSpringeren
dc.rights© Springer International Publishing AG, part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of an article published in Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science, vol 10870. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-92639-1_43en
dc.subjectspamen
dc.subjectsocial spamen
dc.subjectmood analysisen
dc.subjectonline social networksen
dc.subjectYoutubeen
dc.titleA Mood Analysis on Youtube Comments and a Method for Improved Social Spam Detectionen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dcterms.accessRightsinfo:eu-repo/semantics/embargoedAccessen
dcterms.sourceHybrid Artificial Intelligent Systems (HAIS 2018). Pp. 514-525. Lecture Notes in Computer Scienceen
dc.description.versioninfo:eu-repo/semantics/acceptedVersionen
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1007/978-3-319-92639-1_43en
local.relation.projectIDGE/Programa Estatal de Investigacion, Desarrollo e Innovación orientada a los retos de la sociedad en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016, convocatoria del 2017/TIN2017-84658-C2-2-R/Integración de Conocimiento Semántico para el Filtrado de Spam basado en Contenido/SKI4SPAMen
local.relation.projectIDGE/Ayudas para la Excelencia de los Equipos de Investigación avanzada en ciberseguridad/INCIBEC-2015-02495/ES//en
local.relation.projectIDGV/Ikertalde Convocatoria 2016-2021/IT886-16/CAPV/Sistemas Inteligentes para Sistemas Industriales/en
local.embargo.enddate2019-06-08
local.source.detailsVol.10870. Springer,eu_ES


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