Community extraction and visualization in social networks applied to Twitter

Abstract 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.

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