Nuclear energy: Twitter data mining for social listening analysis
dc.contributor.author | Zarrabeitia Bilbao, Enara ![]() | |
dc.contributor.author | Jaca-Madariaga Ominetti, Maite | |
dc.contributor.author | Río Belver, Rosa María ![]() | |
dc.contributor.author | Álvarez Meaza, Izaskun ![]() | |
dc.date.accessioned | 2023-03-06T17:34:14Z | |
dc.date.available | 2023-03-06T17:34:14Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Social Network Analysis and Mining 13 : (2023) // Article ID 29 | es_ES |
dc.identifier.issn | 1869-5450 | |
dc.identifier.issn | 1869-5469 | |
dc.identifier.uri | http://hdl.handle.net/10810/60287 | |
dc.description.abstract | Knowing the presence, attitude and sentiment of society is important to promote policies and actions that influence the development of different energy sources and even more so in the case of an energy source such as nuclear, which has not been without controversy in recent years. The purpose of this paper was to conduct a social listening analysis of nuclear energy using Twitter data mining. A total of 3,709,417 global tweets were analyzed through the interactions and emotions of Twitter users throughout a crucial year: 6 months before and 6 months after the beginning of Russian invasion of Ukraine and the first attack on the Zaporizhzhia NPP. The research uses a novel approach to combine social network analysis methods with the application of artificial neural network models. The results reveal the digital conversation is influenced by the Russian invasion of Ukraine. However, tweets containing personal opinions of influential people also manage to enter the digital conversation, defining the magnitude and direction of the debate. The digital conversation is not constructed as a public argument. Generally, it is a conversation with non-polarized communities (politics, business, science and media); neither armed conflict or military threats against Zaporizhzhia NPP succeed in rousing anti-nuclear voices, even though these events do modify the orientation of the sentiment in the language used, making it more negative. | es_ES |
dc.description.sponsorship | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | nuclear energy | es_ES |
dc.subject | es_ES | |
dc.subject | social network analysis | es_ES |
dc.subject | artificial neural networks | es_ES |
dc.subject | Russia-Ukraine conflict | es_ES |
dc.title | Nuclear energy: Twitter data mining for social listening analysis | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2023. This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s13278-023-01033-8 | es_ES |
dc.identifier.doi | 10.1007/s13278-023-01033-8 | |
dc.departamentoes | Organización de empresas | es_ES |
dc.departamentoeu | Enpresen antolakuntza | es_ES |
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were made. The images or other third party material in this article are
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