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StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams
Yingcai Wu1, Zhutian Chen2, Guodao Sun3
1Computer Science, Zhejiang University, 12377 Hangzhou, Beijing China 310058 (e-mail: wuyingcai@gmail.com).
IEEE Transactions on Visualization and Computer Graphics
|October 21, 2017
Summary
This study introduces StreamExplorer, a framework for analyzing complex social streams on budget PCs. It efficiently detects subevents and clusters tweets using GPU-accelerated Self-Organizing Maps (SOM), aiding crisis management applications.
Area of Science:
- Social Computing
- Data Visualization
- Information Retrieval
Background:
- Analyzing large-scale social streams presents challenges due to diversity, volume, and dynamics.
- Effective exploration is crucial for applications like crisis management.
Purpose of the Study:
- To propose a novel framework for handling complex social streams on budget personal computers (PCs).
- To develop StreamExplorer, a system facilitating visual analysis, tracking, and comparison of social streams at multiple levels.
Main Methods:
- An online method for detecting significant time periods (subevents) within social streams.
- A GPU-assisted Self-Organizing Map (SOM) for stable and efficient clustering of tweets related to subevents.
- StreamExplorer's multi-level visualization: glyph-based timeline (macroscopic), map visualization (mesoscopic), and interactive lenses (microscopic).
Main Results:
- The proposed framework effectively handles complex social streams on budget hardware.
- StreamExplorer provides a multi-faceted overview, topical/geographical summarization, and detailed examination of social streams.
- Case studies and evaluations demonstrate the system's effectiveness and usefulness.
Conclusions:
- The novel framework and StreamExplorer system offer a viable solution for analyzing dynamic social streams.
- The multi-level visualization approach enhances user understanding and exploration of social data.
- The GPU-assisted SOM method ensures efficient processing of large social stream datasets.
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