loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.144.233.198

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Weiler, A.; Grossniklaus, M. and Scholl, M. (2015). The Stor-e-Motion Visualization for Topic Evolution Tracking in Text Data Streams. In Proceedings of the 6th International Conference on Information Visualization Theory and Applications (VISIGRAPP 2015) - IVAPP; ISBN 978-989-758-088-8; ISSN 2184-4321, SciTePress, pages 29-39. DOI: 10.5220/0005292900290039

@conference{ivapp15,
author={Andreas Weiler. and Michael Grossniklaus. and Marc H. Scholl.},
title={The Stor-e-Motion Visualization for Topic Evolution Tracking in Text Data Streams},
booktitle={Proceedings of the 6th International Conference on Information Visualization Theory and Applications (VISIGRAPP 2015) - IVAPP},
year={2015},
pages={29-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005292900290039},
isbn={978-989-758-088-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Information Visualization Theory and Applications (VISIGRAPP 2015) - IVAPP
TI - The Stor-e-Motion Visualization for Topic Evolution Tracking in Text Data Streams
SN - 978-989-758-088-8
IS - 2184-4321
AU - Weiler, A.
AU - Grossniklaus, M.
AU - Scholl, M.
PY - 2015
SP - 29
EP - 39
DO - 10.5220/0005292900290039
PB - SciTePress