Community extraction and visualization in social networks applied to Twitter
Autor: | Benoît Otjacques, Kamel Chelghoum, Francine Herrmann, Imed Kacem, Youcef Abdelsadek |
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Rok vydání: | 2018 |
Předmět: |
Information Systems and Management
Computer science Community extraction 02 engineering and technology 01 natural sciences Theoretical Computer Science World Wide Web Artificial Intelligence 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Social media 010306 general physics Social network analysis Interactive visualization Social network business.industry Community structure Data science Knowledge acquisition Computer Science Applications Visualization Identification (information) Control and Systems Engineering 020201 artificial intelligence & image processing business Software |
Zdroj: | Information Sciences. 424:204-223 |
ISSN: | 0020-0255 |
DOI: | 10.1016/j.ins.2017.09.022 |
Popis: | Nowadays, social network analysis attracts more interest from the scientific community. However, it becomes trickier to analyse the generated data by the social networks due to their complexity, which hides the underlying patterns. In this work we propose an approach for social media analysis, especially for Twitter’s network. Our approach relies on two complementary steps: (i) a community identification based on a new community detection algorithm called Tribase , and (ii) an interactive community visualization, which provides gradual knowledge acquisition using our visualization tool, called NLCOMS . In order to assess the proposed approach, we have tested it on real-world data of the ANR Info-RSN project. This project is related to information propagation and community detection in Twitter’s network, more precisely on a collection of tweets dealing with media articles. The results show that our approach allows us to visually reveal the community structure and the related characteristics. |
Databáze: | OpenAIRE |
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