Paper Info
Title
E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media
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.
Year
DOI
Venue
2017
10.1109/VAST.2017.8585638
2017 IEEE Conference on Visual Analytics Science and Technology (VAST)
Keywords
Field
DocType
Social Media,Event Analysis,Map-like Visual Metaphor,Spatial Temporal Visual Analytics
Data science,Data mining,Data visualization,Social media,Computer science,Visualization,Event evolution,Visual analytics,Event analysis,Semantic space
Conference
ISSN
ISBN
Citations 
2325-9442
978-1-5386-3164-5
5
PageRank 
References 
Authors
0.40
41
6
Name
Order
Citations
PageRank
Siming Chen112514.34
Shuai Chen2339.17
Lijing Lin3241.12
Xiaoru Yuan4115770.28
Jie Liang58610.85
Xiaolong Zhang627821.91