TY - GEN
T1 - E-Map
T2 - 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017
AU - Chen, Siming
AU - Chen, Shuai
AU - Lin, Lijing
AU - Yuan, Xiaoru
AU - Liang, Jie
AU - Zhang, Xiaolong
N1 - Funding Information:
This work is funded by NSFC No. 61672055, NSFC Key Project No. 61232012, and the National Program on Key Basic Research Project (973 Program) No.2015CB352503. This work is also supported by PKU-Qihoo Joint Data Visual Analytics Research Center
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Significant events are often discussed and spread through social media, involving many people. Reposting activities and opinions expressed in social media offer good opportunities to understand the evolution of events. However, the dynamics of reposting activities and the diversity of user comments pose challenges to understand event-related social media data. We propose E-Map, a visual analytics approach that uses map-like visualization tools to help multi-faceted analysis of social media data on a significant event and in-depth understanding of the development of the event. E-Map transforms extracted keywords, messages, and reposting behaviors into map features such as cities, towns, and rivers to build a structured and semantic space for users to explore. It also visualizes complex posting and reposting behaviors as simple trajectories and connections that can be easily followed. By supporting multi-level spatial temporal exploration, E-Map helps to reveal the patterns of event development and key players in an event, disclosing the ways they shape and affect the development of the event. Two cases analysing real-world events confirm the capacities of E-Map in facilitating the analysis of event evolution with social media data.
AB - Significant events are often discussed and spread through social media, involving many people. Reposting activities and opinions expressed in social media offer good opportunities to understand the evolution of events. However, the dynamics of reposting activities and the diversity of user comments pose challenges to understand event-related social media data. We propose E-Map, a visual analytics approach that uses map-like visualization tools to help multi-faceted analysis of social media data on a significant event and in-depth understanding of the development of the event. E-Map transforms extracted keywords, messages, and reposting behaviors into map features such as cities, towns, and rivers to build a structured and semantic space for users to explore. It also visualizes complex posting and reposting behaviors as simple trajectories and connections that can be easily followed. By supporting multi-level spatial temporal exploration, E-Map helps to reveal the patterns of event development and key players in an event, disclosing the ways they shape and affect the development of the event. Two cases analysing real-world events confirm the capacities of E-Map in facilitating the analysis of event evolution with social media data.
UR - http://www.scopus.com/inward/record.url?scp=85060144405&partnerID=8YFLogxK
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U2 - 10.1109/VAST.2017.8585638
DO - 10.1109/VAST.2017.8585638
M3 - Conference contribution
AN - SCOPUS:85060144405
T3 - 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings
SP - 36
EP - 47
BT - 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings
A2 - Fisher, Brian
A2 - Liu, Shixia
A2 - Schreck, Tobias
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 October 2017 through 6 October 2017
ER -