000126835 001__ 126835
000126835 005__ 20240731103356.0
000126835 0247_ $$2doi$$a10.3390/computation11060117
000126835 0248_ $$2sideral$$a134236
000126835 037__ $$aART-2023-134236
000126835 041__ $$aeng
000126835 100__ $$0(orcid)0000-0001-8473-8114$$aQuilez-Robres, Alberto$$uUniversidad de Zaragoza
000126835 245__ $$aSocial networks in military powers: network and sentiment analysis during the Covid-19 pandemic
000126835 260__ $$c2023
000126835 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126835 5203_ $$aThe outbreak of the COVID-19 pandemic shifted socialization and information seeking to social media platforms. The armed forces of the major military powers initiated civil support operations to combat the invisible and common enemy. The aim of this study is to analyze the existence of differential behavior in the corporate profiles of the major military powers on Twitter, Instagram, and Facebook during the COVID-19 pandemic. The principles of social network analysis were followed, along with sentiment analysis, to study web positioning and the emotional content of the posts (N = 25,328). The principles of data mining were applied to process the KPIs (Fanpage Karma), and an artificial intelligence (meaning cloud) sentiment analysis was applied to study the emotionality of the publications. The analysis was carried out using the IBM SPSS Statistics 25 statistical software. Subsequently, a qualitative content analysis was carried out using frequency graphs or word clouds (the application “nubedepalabras” used in English). Significant differences were found between the behavior on social media and the organizational and communicative culture of the nations. It is highlighted that some nations present different preferences from the main communicative strategy developed by their armed forces. Corporate communication of the major military powers should consider the emotional nature of their posts to align with the preferences of their population.
000126835 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000126835 592__ $$a0.409$$b2023
000126835 593__ $$aComputer Science (miscellaneous)$$c2023$$dQ2
000126835 593__ $$aTheoretical Computer Science$$c2023$$dQ3
000126835 593__ $$aModeling and Simulation$$c2023$$dQ3
000126835 593__ $$aApplied Mathematics$$c2023$$dQ3
000126835 594__ $$a3.5$$b2023
000126835 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126835 700__ $$0(orcid)0000-0002-6190-245X$$aAcero-Ferrero, Marian$$uUniversidad de Zaragoza
000126835 700__ $$aDelgado-Bujedo, Diego
000126835 700__ $$0(orcid)0000-0002-0100-1449$$aLozano-Blasco, Raquel$$uUniversidad de Zaragoza
000126835 700__ $$0(orcid)0000-0002-8953-0600$$aAiger-Valles, Montserrat$$uUniversidad de Zaragoza
000126835 7102_ $$14009$$2740$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Psicología Social
000126835 7102_ $$14001$$2625$$aUniversidad de Zaragoza$$bDpto. Ciencias de la Educación$$cÁrea Métod.Invest.Diag.Educac.
000126835 7102_ $$14009$$2735$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Psicolog.Evolut.Educac
000126835 773__ $$g11, 6 (2023), 117 [35 pp.]$$pComputation$$tComputation$$x2079-3197
000126835 8564_ $$s6099285$$uhttps://zaguan.unizar.es/record/126835/files/texto_completo.pdf$$yVersión publicada
000126835 8564_ $$s2647562$$uhttps://zaguan.unizar.es/record/126835/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126835 909CO $$ooai:zaguan.unizar.es:126835$$particulos$$pdriver
000126835 951__ $$a2024-07-31-09:56:46
000126835 980__ $$aARTICLE