DC FieldValueLanguage
dc.contributor.authorGiannakopoulos, Georgios-
dc.contributor.authorDrivas, Ioannis-
dc.contributor.authorKyriaki - Manessi, Daphne-
dc.contributor.authorSakas, Damianos-
dc.date.accessioned2023-10-12T19:57:18Z-
dc.date.available2023-10-12T19:57:18Z-
dc.date.issued2020-04-
dc.identifierscopus-85086234709-
dc.identifier.issn25042289-
dc.identifier.other85086234709-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/234-
dc.description.abstractIn the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.en_US
dc.language.isoenen_US
dc.relation.ispartofBig Data and Cognitive Computingen_US
dc.subjectBig dataen_US
dc.subjectCultural analyticsen_US
dc.subjectCultural dataen_US
dc.subjectPredictive modelingen_US
dc.subjectSearch engine optimizationen_US
dc.subjectSEO factorsen_US
dc.subjectSEO strategyen_US
dc.subjectUser behavioren_US
dc.subjectWebsite load speeden_US
dc.subjectWebsite securityen_US
dc.subjectWebsites visibilityen_US
dc.titleBig data analytics for search engine optimizationen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/bdcc4020005en_US
dc.identifier.scopus2-s2.0-85086234709-
dc.relation.deptDepartment of Archival, Library and Information Studiesen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume4en_US
dc.relation.issue2en_US
dc.identifier.spage1en_US
dc.identifier.epage22en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldSocial Sciencesen_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-1659-3504-
crisitem.author.orcid0000-0003-2407-9502-
crisitem.author.orcid0000-0002-3310-6616-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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