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.identifier.issn | 18770509 | |
dc.identifier.doi | 10.1016/j.procs.2016.05.384 | |
dc.identifier.uri | http://hdl.handle.net/11285/628067 | |
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.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 | Conference Paper | |
dc.identifier.volume | 80 | |
dc.identifier.startpage | 2216 | |
dc.identifier.endpage | 2220 | |
refterms.dateFOA | 2018-04-09T17:15:16Z | |