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Online detection and infographic explanation of spam reviews with data drift adaptation

2024Journal article Open access
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dc.contributor.authorde Arriba Pérez, Francisco
dc.contributor.authorGarcía Méndez, Silvia
dc.contributor.authorLeal, Fátima
dc.contributor.authorMalheiro, Benedita
dc.contributor.authorBurguillo, Juan C.
dc.date.accessioned2024-07-31T09:18:16Z
dc.date.available2024-07-31T09:18:16Z
dc.date.issued2024
dc.descriptionThis work was partially supported by: (i) Xunta de Galicia grants ED481B-2021-118 and ED481B-2022-093, Spain; and (ii) Portuguese national funds through FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) – as part of project UIDP/50014/2020 (https://doi.org/10.54499/UIDP/50014/2020).pt_PT
dc.description.abstractSpam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87 % spam F-measure.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationde Arriba-Pérez, F., García-Méndez, S., Leal, F., Malheiro, B., & Burguillo, J. C. (2024). Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation. Informatica, 1-25. doi:10.15388/24-INFOR562pt_PT
dc.identifier.doi10.15388/24-INFOR562pt_PT
dc.identifier.issn0868-4952
dc.identifier.urihttp://hdl.handle.net/10400.22/25864
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherInstitute of Mathematics & Informaticspt_PT
dc.relationINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
dc.relation.publisherversionhttps://www.informatica.vu.lt/journal/INFORMATICA/article/1338/infopt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectData driftpt_PT
dc.subjectinterpretability and explainabilitypt_PT
dc.subjectNatural Language Processingpt_PT
dc.subjectonlinept_PT
dc.subjectMachine Learningpt_PT
dc.subjectspam detectionpt_PT
dc.titleOnline detection and infographic explanation of spam reviews with data drift adaptationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50014%2F2020/PT
oaire.citation.endPage25pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleInformatica: An international journalpt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamede Arriba Pérez
person.familyNameGarcía Méndez
person.familyNameLeal
person.familyNameBENEDITA CAMPOS NEVES MALHEIRO
person.familyNameBurguillo Rial
person.givenNameFrancisco
person.givenNameSilvia
person.givenNameFátima
person.givenNameMARIA
person.givenNameJuan Carlos
person.identifier.ciencia-id2211-3EC7-B4B6
person.identifier.ciencia-id7A15-08FC-4430
person.identifier.orcid0000-0002-1140-679X
person.identifier.orcid0000-0003-0533-1303
person.identifier.orcid0000-0003-4418-2590
person.identifier.orcid0000-0001-9083-4292
person.identifier.orcid0000-0001-9869-7448
person.identifier.ridD-2450-2018
person.identifier.ridABF-4227-2020
person.identifier.ridY-3460-2019
person.identifier.ridE-9091-2016
person.identifier.scopus-author-id56891654000
person.identifier.scopus-author-id57201127684
person.identifier.scopus-author-id57190765181
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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