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Defining Dynamic Indicators for Social Network Analysis: A Case Study in the Automotive Domain using Twiter
dc.contributor.author | Lanza Cruz, Indira Lázara | |
dc.contributor.author | Berlanga Llavori, Rafael | |
dc.date.accessioned | 2020-07-24T11:20:36Z | |
dc.date.available | 2020-07-24T11:20:36Z | |
dc.date.issued | 2018-09-18 | |
dc.identifier.citation | 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: 10.5220/0006932902210228 | ca_CA |
dc.identifier.isbn | 978-989-758-330-8 | |
dc.identifier.uri | http://hdl.handle.net/10234/189276 | |
dc.description | Comunicación 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.abstract | 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. | ca_CA |
dc.format.extent | 8 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | SciTePress | ca_CA |
dc.relation.isPartOf | 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 978-989-758-330-8 | ca_CA |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | social business intelligence | ca_CA |
dc.subject | indicators | ca_CA |
dc.subject | data streaming | ca_CA |
dc.title | Defining Dynamic Indicators for Social Network Analysis: A Case Study in the Automotive Domain using Twiter | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.5220/0006932902210228 | |
dc.relation.projectID | 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) | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.scitepress.org/Link.aspx?doi=10.5220%2f0006932902210228 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |