2016Procedia Computer ScienceOpen access

Social Media Conversation Monitoring: Visualize Information Contents of Twitter Messages Using Conversational Metrics

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

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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.

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What this paper is about

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.

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Computer science, Conversation, Social media, Visualization, World Wide Web, Microblogging, Human–computer interaction, Data science

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