Social networks in military powers: network and sentiment analysis during the Covid-19 pandemic

Quilez-Robres, Alberto (Universidad de Zaragoza) ; Acero-Ferrero, Marian (Universidad de Zaragoza) ; Delgado-Bujedo, Diego ; Lozano-Blasco, Raquel (Universidad de Zaragoza) ; Aiger-Valles, Montserrat (Universidad de Zaragoza)

Resumen: The 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.

Idioma: Inglés

DOI: 10.3390/computation11060117

Año: 2023

Publicado en: Computation 11, 6 (2023), 117 [35 pp.]
ISSN: 2079-3197

Factor impacto CITESCORE: 3.5 - Applied Mathematics (Q2) - Theoretical Computer Science (Q2) - Computer Science (all) (Q2) - Modeling and Simulation (Q2)

Factor impacto SCIMAGO: 0.409 - Computer Science (miscellaneous) (Q2) - Theoretical Computer Science (Q3) - Modeling and Simulation (Q3) - Applied Mathematics (Q3)

Tipo y forma: Article (Published version)

Área (Departamento): Área Psicología Social (Dpto. Psicología y Sociología)
Área (Departamento): Área Métod.Invest.Diag.Educac. (Dpto. Ciencias de la Educación)
Área (Departamento): Área Psicolog.Evolut.Educac (Dpto. Psicología y Sociología)