E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media

Siming Chen, Shuai Chen, Lijing Lin, Xiaoru Yuan, Jie Liang, Xiaolong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings
EditorsTobias Schreck, Brian Fisher, Shixia Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages36-47
Number of pages12
ISBN (Electronic)9781538631638
DOIs
StatePublished - Dec 21 2018
Event2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Phoenix, United States
Duration: Oct 1 2017Oct 6 2017

Publication series

Name2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings

Other

Other2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017
CountryUnited States
CityPhoenix
Period10/1/1710/6/17

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Information Systems
  • Media Technology

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    Chen, S., Chen, S., Lin, L., Yuan, X., Liang, J., & Zhang, X. (2018). E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media. In T. Schreck, B. Fisher, & S. Liu (Eds.), 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings (pp. 36-47). [8585638] (2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2017.8585638