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dc.contributor.author | Forciniti, Alessia | es_ES |
dc.contributor.author | Marletta, Andrea | es_ES |
dc.contributor.author | Moretti, Magda | es_ES |
dc.date.accessioned | 2024-09-26T07:56:13Z | |
dc.date.available | 2024-09-26T07:56:13Z | |
dc.date.issued | 2024-07-16 | |
dc.identifier.isbn | 9788413962016 | |
dc.identifier.uri | http://hdl.handle.net/10251/208668 | |
dc.description.abstract | [EN] The use of non-official data sources as Twitter has been implemented for the monitoring of social and public events in many different fields during last years. Following this issue, this work proposes to analyse a very well-known musical event, the Eurovision Song Contest (ESC) 2022 using tweets pooled by the official hashtag of the competition. From a methodological point of view, text mining techniques have been applied to detect the most influencing terms and topics tweeted by users during the show and to compare the official results of the contest with a ranking only based on the appreciation of the Twitter users on posts relative to the participant countries. | es_ES |
dc.format.extent | 9 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Editorial Universitat Politècnica de València | es_ES |
dc.relation.ispartof | 6th International Conference on Advanced Research Methods and Analytics (CARMA 2024) | |
dc.rights | Reconocimiento - No comercial - Compartir igual (by-nc-sa) | es_ES |
dc.subject | Twitter Data | es_ES |
dc.subject | Eurovision Song Contest | es_ES |
dc.subject | Text Mining | es_ES |
dc.title | The use of non-official data source for the analysis of public events: evidences from the Eurovision Song Contest 2022 | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.identifier.doi | 10.4995/CARMA2024.2024.17824 | |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Forciniti, A.; Marletta, A.; Moretti, M. (2024). The use of non-official data source for the analysis of public events: evidences from the Eurovision Song Contest 2022. Editorial Universitat Politècnica de València. 34-42. https://doi.org/10.4995/CARMA2024.2024.17824 | es_ES |
dc.description.accrualMethod | OCS | es_ES |
dc.relation.conferencename | CARMA 2024 - 6th International Conference on Advanced Research Methods and Analytics | es_ES |
dc.relation.conferencedate | Junio 26-28, 2024 | es_ES |
dc.relation.publisherversion | http://ocs.editorial.upv.es/index.php/CARMA/CARMA2024/paper/view/17824 | es_ES |
dc.description.upvformatpinicio | 34 | es_ES |
dc.description.upvformatpfin | 42 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | OCS\17824 | es_ES |