Interactive Visual Analysis of High Throughput Text Streams

Conference · Sun Jan 01 04:00:00 EST 2012

OSTI ID:1055101

Steed, Chad A ^[1]; Potok, Thomas E ^[1]; Patton, Robert M ^[1]; Goodall, John R ^[1]; Maness, Christopher S ^[1]; Senter, James K ^[1]; Potok, Thomas E ^[1]

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1. ORNL

The scale, velocity, and dynamic nature of large scale social media systems like Twitter demand a new set of visual analytics techniques that support near real-time situational awareness. Social media systems are credited with escalating social protest during recent large scale riots. Virtual communities form rapidly in these online systems, and they occasionally foster violence and unrest which is conveyed in the users language. Techniques for analyzing broad trends over these networks or reconstructing conversations within small groups have been demonstrated in recent years, but state-of- the-art tools are inadequate at supporting near real-time analysis of these high throughput streams of unstructured information. In this paper, we present an adaptive system to discover and interactively explore these virtual networks, as well as detect sentiment, highlight change, and discover spatio- temporal patterns.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: ORNL LDRD Director's R&D; ORNL work for others
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1055101
• Country of Publication: United States
• Language: English

Related Subjects

sentiment analysis
social media
streaming information.
text mining
visual analytics