Defining Dynamic Indicators for Social Network Analysis: A Case Study in the Automotive Domain using Twiter
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/159830
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Defining Dynamic Indicators for Social Network Analysis: A Case Study in the Automotive Domain using TwiterFecha de publicación
2018-09-18Editor
SciTePressISBN
978-989-758-330-8Cita bibliográfica
Lanza Cruz, I. and Berlanga Llavori, R. (2018). Defining Dynamic Indicators for Social Network Analysis: A Case Study in the Automotive Domain using Twitter.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 221-228. DOI: 10.5220/0006932902210228Tipo de documento
info:eu-repo/semantics/conferenceObjectVersión de la editorial
https://www.scitepress.org/Link.aspx?doi=10.5220%2f0006932902210228Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
In this paper we present a framework based on Linked Open Data Infrastructures to perform analysis tasks in social networks based on dynamically defined indicators. Based on the typical stages of business intelligence ... [+]
In this paper we present a framework based on Linked Open Data Infrastructures to perform analysis tasks in social networks based on dynamically defined indicators. Based on the typical stages of business intelligence models, which starts from the definition of strategic goals to define relevant indicators (Key Performance Indicators), we propose a new scenario where the sources of information are the social networks. The fundamental contribution of this work is to provide a framework for easily specifying and monitoring social indicators based on the measures offered by the APIs of the most important social networks. The main novelty of this method is that all the involved data and information is represented and stored as Linked Data. In this work we demonstrate the benefits of using linked open data, especially for processing and publishing company-specific social metrics and indicators. [-]
Descripción
Comunicación pesentada en 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2018) (18-20 septiembre Sevilla, España)
Publicado en
Ana Fred ; Joaquim Filipe (Eds.). Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2018), Vol. 1: ScitePress, 2018. ISBN 978-989-758-330-8Proyecto de investigación
Ministry of Economy and Trade with the project of the National R&D Plan with contract number TIN2017-88805-R) ; Universitat Jaume I, pre-doctoral scholarship programme (PREDOC/2017/28)Derechos de acceso
info:eu-repo/semantics/openAccess
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