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

Carlo Lipizzi; Dante Gama Dessavre; Luca Iandoli; José Emmanuel Ramirez Marquez

doi:10.1016/j.procs.2016.05.384

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

Carlo Lipizzi
, Dante Gama Dessavre
, Luca Iandoli
, José Emmanuel Ramirez Marquez

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	2216-2220
Number of pages	5
Journal	Procedia Computer Science
Volume	80
DOIs	https://doi.org/10.1016/j.procs.2016.05.384
State	Published - 2016
Event	International Conference on Computational Science, ICCS 2016 - San Diego, United States Duration: 6 Jun 2016 → 8 Jun 2016

Keywords

Content analysis
Conversation analysis
Social media
Text mining
Visualization

Access to Document

10.1016/j.procs.2016.05.384

Cite this

@article{c82afc7c5eea46089532a9a2c8586c4f,

title = "Social media conversation monitoring: Visualize information contents of twitter messages using conversational metrics",

abstract = "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.",

keywords = "Content analysis, Conversation analysis, Social media, Text mining, Visualization",

author = "Carlo Lipizzi and Dessavre, \{Dante Gama\} and Luca Iandoli and Marquez, \{Jos{\'e} Emmanuel Ramirez\}",

note = "Publisher Copyright: {\textcopyright} The Authors. Published by Elsevier B.V.; International Conference on Computational Science, ICCS 2016 ; Conference date: 06-06-2016 Through 08-06-2016",

year = "2016",

doi = "10.1016/j.procs.2016.05.384",

language = "English",

volume = "80",

pages = "2216--2220",

}

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 - https://www.scopus.com/pages/publications/84978521835

UR - https://www.scopus.com/pages/publications/84978521835#tab=citedBy

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 -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this