Show simple item record

dc.contributor.advisorhttp://creativecommons.org/licenses/by/4.0/
dc.contributor.authorZarrabeitia Bilbao, Enara ORCID
dc.contributor.authorMorales Gras, Jordi
dc.contributor.authorRío Belver, Rosa María ORCID
dc.contributor.authorGarechana Anacabe, Gaizka ORCID
dc.date.accessioned2024-03-19T19:14:51Z
dc.date.available2024-03-19T19:14:51Z
dc.date.issued2022-02-26
dc.identifier.citationEl Profesional de la información 31(1) : (2022) // Article ID e310114.es_ES
dc.identifier.issn1699-2407
dc.identifier.urihttp://hdl.handle.net/10810/66223
dc.description.abstractThis study analyzes Twitter’s contribution to green energy. More than 200,000 global tweets sent during 2020 containing the terms “green energy” OR “greenenergy” were analyzed. The tweets were captured by web scraping and processed using algorithms and techniques for the analysis of massive datasets from social networks. In particular, relationships between users (through mentions) were determined according to the Louvain multilevel algorithm to identify communities and analyze global (density and centralization) and node-level (centrality) metrics. Subsequently, the content of the conversation was subject to semantic analysis (co-occurrence of the most relevant words), hashtag analysis (frequency analysis), and sentiment analysis (using the VADER model). The results reveal nine main communities and their leaders, as well as three main topics of conversation and the emotional state of the digital discussion. The main communities revolve around politics, socioeconomic issues, and environmental activism, while the conversations, which have developed mostly in positive terms, focus on green energy sources and storage, being aligned with the main communities identified, i.e., on political, socioeconomic, and climate change issues. Although most of the conversations have been about socioeconomic issues, the presence of leading company accounts was minor. The main aim of this work is to take the first steps toward an innovative competitive intelligence methodology to study and determine trends within different scientific fields or technologies in society that will enable strategic decisions to be made.es_ES
dc.language.isoenges_ES
dc.language.isospa
dc.publisherEPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectgreen energyes_ES
dc.subjectTwitteres_ES
dc.subjectsocial network analysises_ES
dc.subjectsemantic analysises_ES
dc.subjectsentiment analysises_ES
dc.subjectbig dataes_ES
dc.subjectbusiness intelligencees_ES
dc.subjectdata analyticses_ES
dc.subjecttext analyticses_ES
dc.subjectsocial analyticses_ES
dc.subjectsocial networkses_ES
dc.subjectsocial mediaes_ES
dc.subjectenvironmentes_ES
dc.subjectrenewable energyes_ES
dc.titleGreen energy: identifying development trends in society using Twitter data mining to make strategic decisionses_ES
dc.title.alternativeEnergía verde: identificación de tendencias en la sociedad mediante la minería de datos aplicada a Twitter para la toma de decisiones estratégicases_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder(c) 2022 EPI con una licencia Creative Commons BY.es_ES
dc.relation.publisherversionhttps://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86560es_ES
dc.identifier.doi10.3145/epi.2022.ene.14
dc.departamentoesOrganización de empresases_ES
dc.departamentoeuEnpresen antolakuntzaes_ES


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record