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dc.contributor.authorRabadán Martín, Inmaculadaes-ES
dc.contributor.authorBarcos Redín, Lucíaes-ES
dc.contributor.authorPereira Delgado, Jorgees-ES
dc.contributor.authorAguado Correa, Franciscoes-ES
dc.contributor.authorPadilla Garrido, Nuriaes-ES
dc.date.accessioned2024-09-03T08:16:20Z
dc.date.available2024-09-03T08:16:20Z
dc.date.issued2024-02-25es_ES
dc.identifier.issn0261-5177es_ES
dc.identifier.urihttps://doi.org/10.1016/j.tourman.2024.104981es_ES
dc.descriptionArtículos en revistases_ES
dc.description.abstract.es-ES
dc.description.abstractIn a social-media context, brands need to understand how to frame their messages, so that a topic can be quickly recognized, promoting higher levels of user engagement. However, knowledge about the link between content type and its engagement is not sufficiently studied. We first explore hotel Firm-Generated Content (FGC) and its inherent themes using topic modelling; we then use an ad hoc metric to investigate the engagement levels associated with each topic; thirdly, we compare the relevance attached to the topics with their engagement levels. In total, 44,448 corporate tweets from 62 hotel brands were analyzed to identify 14 topics, one of which had not previously been uncovered. Notably, there was a positive correlation with engagement for content related to hotel management activities and, among smaller groups, to sustainability. The results will expand FGC-related investigation within the hotel sector and will be of interest to firms seeking effective communication strategies.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.rightsCreative Commons Reconocimiento-NoComercial-SinObraDerivada Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/es_ES
dc.sourceRevista: Tourism Management, Periodo: 1, Volumen: 106, Número: 104981, Página inicial: 104981, Página final: .es_ES
dc.subject.otherInnovación docente y Analytics (GIIDA)es_ES
dc.titleTopic-based engagement analysis: Focusing on hotel industry Twitter accountses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywords.es-ES
dc.keywordsFirm-generated contentTwitterNatural language processing Topic modelling Latent Dirichlet allocation Engagement Hotelen-GB


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