TY - JOUR
T1 - Social media conversation monitoring
T2 - International Conference on Computational Science, ICCS 2016
AU - Lipizzi, Carlo
AU - Dessavre, Dante Gama
AU - Iandoli, Luca
AU - Marquez, José Emmanuel Ramirez
N1 - Publisher Copyright:
© The Authors. Published by Elsevier B.V.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Content analysis
KW - Conversation analysis
KW - Social media
KW - Text mining
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=84978521835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978521835&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2016.05.384
DO - 10.1016/j.procs.2016.05.384
M3 - Conference article
AN - SCOPUS:84978521835
SN - 1877-0509
VL - 80
SP - 2216
EP - 2220
JO - Procedia Computer Science
JF - Procedia Computer Science
Y2 - 6 June 2016 through 8 June 2016
ER -