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2016 IEEE Conference on Visual Analytics Science and Technology (VAST) (2016)
Baltimore, MD, USA
Oct. 23, 2016 to Oct. 28, 2016
ISBN: 978-1-5090-5662-0
pp: 41-50
Siming Chen , Key Laboratory of Machine Perception (Ministry of Education), Peking University, China
Shuai Chen , Key Laboratory of Machine Perception (Ministry of Education), Peking University, China
Zhenhuang Wang , Key Laboratory of Machine Perception (Ministry of Education), Peking University, China
Jie Liang , Faculty of Engineer and Information Technology, The University of Technology, Sydney, Australia
Xiaoru Yuan , Key Laboratory of Machine Perception (Ministry of Education), Peking University, China
Nan Cao , New York University, Shanghai, China
Yadong Wu , Southwest University of Science and Technology, China
ABSTRACT
Popular social media platforms could rapidly propagate vital information over social networks among a significant number of people. In this work we present D-Map (Diffusion Map), a novel visualization method to support exploration and analysis of social behaviors during such information diffusion and propagation on typical social media through a map metaphor. In D-Map, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected and mapped to a hexagonal grid based on their behavior similarities and in chronological order of the repostings. With additional interaction and linking, D-Map is capable of providing visual portraits of the influential users and describing their social behaviors. A comprehensive visual analysis system is developed to support interactive exploration with D-Map. We evaluate our work with real world social media data and find interesting patterns among users. Key players, important information diffusion paths, and interactions among social communities can be identified.
INDEX TERMS
Data visualization, Diffusion processes, Visualization, Twitter, Clutter, Radar
CITATION

S. Chen et al., "D-Map: Visual analysis of ego-centric information diffusion patterns in social media," 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), Baltimore, MD, USA, 2016, pp. 41-50.
doi:10.1109/VAST.2016.7883510
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