Social media conversation monitoring: Visualize information contents of twitter messages using conversational metrics

dc.creator	Lipizzi C.
dc.creator	Dessavre D.G.
dc.creator	Iandoli L.
dc.creator	Marquez J.E.R.
dc.date	2016
dc.date.accessioned	2018-04-09T17:15:16Z
dc.date.available	2018-04-09T17:15:16Z
dc.description	In this paper we present a novel method to extract and visualize actionable information from streams of social media messages, analyzed as conversational elements. Our method has been applied to over 4 million messages related to more than 35 different events, demonstrating good results identifying conversational patterns. © The Authors. Published by Elsevier B.V.
dc.identifier.doi	10.1016/j.procs.2016.05.384
dc.identifier.endpage	2220
dc.identifier.issn	18770509
dc.identifier.startpage	2216
dc.identifier.uri	http://hdl.handle.net/11285/628067
dc.identifier.volume	80
dc.language	eng
dc.publisher	Elsevier B.V.
dc.relation	https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978521835&doi=10.1016%2fj.procs.2016.05.384&partnerID=40&md5=83d596c982f7bbe8131b681aec2ed447
dc.rights	openAccess
dc.source	Procedia Computer Science
dc.source	Scopus
dc.subject	Data mining
dc.subject	Flow visualization
dc.subject	Content analysis
dc.subject	Conversation analysis
dc.subject	Information contents
dc.subject	Social media
dc.subject	Text mining
dc.subject	Social networking (online)
dc.title	Social media conversation monitoring: Visualize information contents of twitter messages using conversational metrics
dc.type	Conferencia
refterms.dateFOA	2018-04-09T17:15:16Z

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