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dc.contributor.authorLanza Cruz, Indira Lázara
dc.contributor.authorBerlanga Llavori, Rafael
dc.date.accessioned2020-07-24T11:20:36Z
dc.date.available2020-07-24T11:20:36Z
dc.date.issued2018-09-18
dc.identifier.citationLanza 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: 10.5220/0006932902210228ca_CA
dc.identifier.isbn978-989-758-330-8
dc.identifier.urihttp://hdl.handle.net/10234/189276
dc.descriptionComunicación pesentada en 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2018) (18-20 septiembre Sevilla, España)ca_CA
dc.description.abstractIn 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.ca_CA
dc.format.extent8 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSciTePressca_CA
dc.relation.isPartOfAna 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 978-989-758-330-8ca_CA
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectsocial business intelligenceca_CA
dc.subjectindicatorsca_CA
dc.subjectdata streamingca_CA
dc.titleDefining Dynamic Indicators for Social Network Analysis: A Case Study in the Automotive Domain using Twiterca_CA
dc.typeinfo:eu-repo/semantics/conferenceObjectca_CA
dc.identifier.doihttp://dx.doi.org/10.5220/0006932902210228
dc.relation.projectIDMinistry 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)ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.scitepress.org/Link.aspx?doi=10.5220%2f0006932902210228ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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