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      Semantic analysis and the evolution towards participative branding: Do locals communicate the same destination brand values as DMOs?

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      PLoS ONE
      Public Library of Science

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          Abstract

          Participative branding is a process by which DMOs, locals and visitors contribute, through their activity in social media, to the definition of the emotional values associated to a destination. From the communicative point of view it is particularly important that DMOs and local citizens work together in the transmission of a set of coherent values that compose a personalised identity. This paper presents a novel methodology, based on semantic similarity measures, that permits to make an automated analysis of the emotional values transmitted by official tourist offices and by local citizens through social media. A study of 54,000 tweets from 9 main European tourist destinations highlights the lack of a strategy towards the communication of a distinctive brand and a strong gap between the official view transmitted by the DMOs of the destinations and the one communicated by their residents.

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          Most cited references61

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          Consumer Brand Engagement in Social Media: Conceptualization, Scale Development and Validation

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            When Good Brands Do Bad

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              The Impact of New Media on Customer Relationships

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                Author and article information

                Contributors
                Role: Data curationRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: ResourcesRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2018
                7 November 2018
                : 13
                : 11
                : e0206572
                Affiliations
                [1 ] TAKA (Intelligent Technologies for Advanced Knowledge Acquisition) research group. Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain
                [2 ] Departament d’Estudis de Comunicació, Universitat Rovira i Virgili, Tarragona, Spain
                [3 ] Computer Science Department, Hodeidah University, Hodeidah, Yemen
                The University of Queensland, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9774-9066
                Article
                PONE-D-17-38749
                10.1371/journal.pone.0206572
                6221313
                30403764
                af97ec34-408d-40d2-a3a1-108e36679c56
                © 2018 Jabreel et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 31 October 2017
                : 16 October 2018
                Page count
                Figures: 5, Tables: 7, Pages: 29
                Funding
                The authors acknowledge the support of Univ. Rovira i Virgili through a Martí Franquès PhD grant, the assistant/teaching grant for the Department of Computer Engineering and Mathematics and the Research Support Funds 2016PFR-URV-B2-60.
                Categories
                Research Article
                Biology and Life Sciences
                Psychology
                Emotions
                Social Sciences
                Psychology
                Emotions
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Twitter
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Social Sciences
                Linguistics
                Semantics
                Biology and Life Sciences
                Psychology
                Personality
                Social Sciences
                Psychology
                Personality
                Physical Sciences
                Mathematics
                Statistics
                Similarity Measures
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Facebook
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Facebook
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Facebook
                Custom metadata
                According to Twitter agreement and policy, we are restricted to redistribute or publish the data. So, we provided the dataset of IDs of analysed tweets in this GitHub repository: ( https://github.com/mhjabreel/BrandSemData). The tweets can be downloaded given their IDs using the following API: ( https://developer.twitter.com/en/docs/tweets/post-and-engage/apireference/get-statuses-show-id). Another option is to use the TwitterCrawler from this GitHub repo: ( https://raw.githubusercontent.com/ywwbill/TwitterCrawler).

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