A Data Quality Multidimensional Model for Social Media Analysis
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Aramburu Cabo, María José; Berlanga Llavori, Rafael; Lanza Cruz, Indira Lázara
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
A Data Quality Multidimensional Model for Social Media AnalysisFecha de publicación
2023-11-10Editor
SpringerISSN
1867-0202; 2363-7005Cita bibliográfica
Aramburu, M.J., Berlanga, R. & Lanza-Cruz, I. A Data Quality Multidimensional Model for Social Media Analysis. Bus Inf Syst Eng (2023). https://doi.org/10.1007/s12599-023-00840-9Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s12599-023-00840-9Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Social media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development ... [+]
Social media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development of practical business intelligence solutions. This paper aims at building high-quality datasets for social business intelligence (SoBI). The proposed method offers an integrated and dynamic approach to identify the relevant quality metrics for each analysis domain. This method employs a novel multidimensional data model for the construction of cubes with impact measures for various quality metrics. In this model, quality metrics and indicators are organized in two main axes. The first one concerns the kind of facts to be extracted, namely: posts, users, and topics. The second axis refers to the quality perspectives to be assessed, namely: credibility, reputation, usefulness, and completeness. Additionally, quality cubes include a user-role dimension so that quality metrics can be evaluated in terms of the user business roles. To demonstrate the usefulness of this approach, the authors have applied their method to two separate domains: automotive business and natural disasters management. Results show that the trade-off between quantity and quality for social media data is focused on a small percentage of relevant users. Thus, data filtering can be easily performed by simply ranking the posts according to the quality metrics identified with the proposed method. As far as the authors know, this is the first approach that integrates both the extraction of analytical facts and the assessment of social media data quality in the same framework. [-]
Publicado en
Business & Information Systems Engineering (2023)Derechos de acceso
© The Author(s) 2023
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- LSI_Articles [362]
- ICC_Articles [415]