Authors:
Andreas Weiler
;
Michael Grossniklaus
and
Marc H. Scholl
Affiliation:
University of Konstanz, Germany
Keyword(s):
Story Visualization, Text Data Streams, Twitter.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Information and Scientific Visualization
;
Text and Document Visualization
;
Time-Dependent Visualization
Abstract:
Nowadays, there are plenty of sources generating massive amounts of text data streams in a continuous way.
For example, the increasing popularity and the active use of social networks result in voluminous and fast-flowing
text data streams containing a large amount of user-generated data about almost any topic around the
world. However, the observation and tracking of the ongoing evolution of topics in these unevenly distributed
text data streams is a challenging task for analysts, news reporters, or other users. This paper presents “Stor-e-
Motion” a shape-based visualization to track the ongoing evolution of topics’ frequency (i.e., importance),
sentiment (i.e., emotion), and context (i.e., story) in user-defined topic channels over continuous flowing text
data streams. The visualization supports the user in keeping the overview over vast amounts of streaming
data and guides the perception of the user to unexpected and interesting points or periods in the text data
stream. In this
work, we mainly focus on the visualization of text streams from the social microblogging
service Twitter, for which we present a series of case studies (e.g., the observation of cities, movies, or natural
disasters) applied on real-world data streams collected from the public timeline. However, to further evaluate
our visualization, we also present a baseline case study applied on the text stream of a fantasy book series.
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