TweetProbe: A Real-Time Microblog Stream Visualization Framework
As the importance of social media increases in our daily life, most adopters witness its significant impact on numerous practices in different areas such as business marketing, journalism, entertainment, and social sciences. However, the enormous amount of data makes the overall content difficult to assess and comprehend for both users and information analysts, raising scalability issues. Furthermore, timely understanding of trending topics is a crucial element due to the short life characteristic of most topics in microblogs. In this paper, we present a novel data visualization approach for real-time social data stream analytics using Twitter streaming data. The visual and architectural design of the system has been implemented as a real-time visualization framework, showing the most trendy tweets, hashtags and sentiment of individual messages. The framework proposed in this paper showcases visualization of real-time message streams through different presentation methods with animation effects highlighting the nature of live information streams. Several scenarios are provided as examples of possible application of this system, including deployment as an information canvas that provides an overview of currently trending topics as a wall-sized interactive media arts installation.
Media Art: Ambient Visualization
- ‘TweetProbe: A Real-Time Microblog Stream Visualization Framework’, IEEE VIS 2013 Art Show. (Oct. 13 – 18, 2013)
- Media Art Work ‘Korea-2012’ by George Legrady and Byungkyu Kang had been invited to the exhibition ‘Data Curation’ at Seoul National University Museum of Art. (May – Aug 2013)
- ‘Making Visible the Invisible’: Experimental Data Visualization Project
Making Visible the Invisible: Seattle Public Library Visualization Project
The visualization Samples or Screenshots displayed below are from the Seattle Public Library DataViz project (as a course project of MAT259). The visualizations in this section are programmed in Processing + Java. In the frequent keywords visualization, Natural Language ToolKit Library (NLTK) is also used in order to detect stopwords in the result.
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