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Visual Interactive Approach for Mining Twitter’s Networks

Abstract : Understanding the semantic behind relational data is very challenging, especially, when it is tricky to provide efficient analysis at scale. Furthermore, the complexity is also driven by the dynamical nature of data. Indeed, the analysis given at a specific time point becomes unsustainable even incorrect over time. In this paper, we rely on a visual interactive approach to handle Twitter’s networks using NLCOMS. NLCOMS provides multiple and coordinated views in order to grasp the underlying information. Finally, the applicability of the proposed approach is assessed on real-world data of the ANR-Info-RSN project.
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Submitted on : Friday, May 4, 2018 - 5:17:28 PM
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  • HAL Id : hal-01785938, version 1



Youcef Abdelsadek, Kamel Chelghoum, Francine Herrmann, Imed Kacem, Benoit Otjacques. Visual Interactive Approach for Mining Twitter’s Networks. International Conference on Data Mining and Big Data DMBD 2016: Data Mining and Big Data, Jun 2016, Bali, Indonesia. pp.342-349. ⟨hal-01785938⟩



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