research-article

Watch the Story Unfold with TextWheel: Visualization of Large-Scale News Streams

Authors:

Steve SkienaAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 3, Issue 2

Article No.: 20, Pages 1 - 17

https://doi.org/10.1145/2089094.2089096

Published: 01 February 2012 Publication History

Abstract

Keyword-based searching and clustering of news articles have been widely used for news analysis. However, news articles usually have other attributes such as source, author, date and time, length, and sentiment which should be taken into account. In addition, news articles and keywords have complicated macro/micro relations, which include relations between news articles (i.e., macro relation), relations between keywords (i.e., micro relation), and relations between news articles and keywords (i.e., macro-micro relation). These macro/micro relations are time varying and pose special challenges for news analysis.

In this article we present a visual analytics system for news streams which can bring multiple attributes of the news articles and the macro/micro relations between news streams and keywords into one coherent analytical context, all the while conveying the dynamic natures of news streams. We introduce a new visualization primitive called TextWheel which consists of one or multiple keyword wheels, a document transportation belt, and a dynamic system which connects the wheels and belt. By observing the TextWheel and its content changes, some interesting patterns can be detected. We use our system to analyze several news corpora related to some major companies and the results demonstrate the high potential of our method.

References

[1]

Albrecht-Buehler, C., Watson, B., and Shamma, D. 2005. Visualizing live text streams using motion and temporal pooling. IEEE Comput. Graph. Appl. 52--59.

Digital Library

[2]

Allan, J., Harding, S., Fisher, D., Bolivar, A., Guzman-Lara, S., and Amstutz, P. 2005. Taking topic detection from evaluation to practice. In Proceedings of the Hawaii International Conference on System Sciences. 101--101.

Digital Library

[3]

Bautin, M., Ward, C., Patil, A., and Skiena, S. 2010. Access: News and blog analysis for the social sciences. In Proceedings of the 19th International Conference on World Wide Web. ACM, 1229--1232.

Digital Library

[4]

Brill, E. 1994. Some advances in rule-based part of speech tagging. In Proccedings of the 12th National Conference on Artificial Intelligence.

Digital Library

[5]

Chieu, H. and Ng, H. 2002. Named entity recognition: A maximum entropy approach using global information. In Proceedings of the International Conference on Computational Linguistics. 190--196.

Digital Library

[6]

Cover, T. M. and Thomas, J. A. 2006. Elements of Information Theory 2nd Ed. Wiley-Interscience.

Digital Library

[7]

Erten, C., Harding, P., Kobourov, S., Wampler, K., and Yee, G. 2004. Exploring the computing literature using temporal graph visualization. In Proceedings of the Conference on Visualization and Data Analysis (VDA).

[8]

Glance, N. S., Hurst, M., and Tomokiyo, T. 2004. Blogpulse: Automated trend discovery for weblogs. In Proceedings of the Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics.

[9]

Granitzer, M., Kienreich, W., Sabol, V., Andrews, K., and Klieber, W. 2004. Evaluating a system for interactive exploration of large, hierarchically structured document repositories. In Proceedings of the IEEE Symposium on Information Visualization. 127--134.

Digital Library

[10]

Havre, S., Hetzler, B., and Nowell, L. 2000. Themeriver: Visualizing theme changes over time. In Proceedings of the IEEE Symposium on Information Visualization. 115--123.

Digital Library

[11]

Hetzler, E. G., Crow, V. L., Payne, D. A., and Turner, A. E. 2005. Turning the bucket of text into a pipe. In Proceedings of the IEEE Symposium on Information Visualization (INFOVIS’05). IEEE Computer Society. 89--94.

Digital Library

[12]

Lloyd, L., Kechagias, D., and Skiena, S. 2005. Lydia: A system for large-scale news analysis. In Proceedings of the 12th Symposium of String Processing and Information Retrieval (SPIRE’05). Lecture Notes in Computer Science, vol. 3772. Springer, 161--166.

Digital Library

[13]

Mao, Y., Dillon, J., and Lebanon, G. 2007. Sequential document visualization. IEEE Trans. Vis. Comput. Graph. 13, 6, 1208--1215.

Digital Library

[14]

McDonald, D. 1993. Internal and external evidence in the identification and semantic categorization of proper names. In Proceedings of the Workshop on Acquisition of Lexical Knowledge from Text. 32--43.

[15]

Mitchell, T. 1997. Machine Learning. McGraw-Hill.

Digital Library

[16]

Oelke, D., Bak, P., Keim, D., Last, M., and Danon, G. 2008. Visual evaluation of text features for document summarization and analysis. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology. 75--82.

[17]

Pang, B. and Lee, L. 2008. Opinion Mining and Sentiment Analysis. Now Publishers.

[18]

Paulovich, F. V. and Minghim, R. 2008. Hipp: A novel hierarchical point placement strategy and its application to the exploration of document collections. IEEE Trans. Vis. Comput. Graph. 14, 6, 1229--1236.

Digital Library

[19]

Stasko, J., Görg, C., and Liu, Z. 2008. Jigsaw: Supporting investigative analysis through interactive visualization. Inf. Vis. 7, 2, 118--132.

Digital Library

[20]

van Ham, F., Wattenberg, M., and Viegas, F. B. 2009. Mapping text with phrase nets. IEEE Trans. Vis. Comput. Graph. 15, 6, 1169--1176.

Digital Library

[21]

Wattenberg, M. and Viégas, F. B. 2008. The word tree, an interactive visual concordance. IEEE Trans. Vis. Comput. Graph. 14, 6, 1221--1228.

Digital Library

[22]

Wise, J. A. 1999. The ecological approach to text visualization. J. Amer. Soc. Inf. Sci. 50, 13, 1224--1233.

Digital Library

[23]

Wong, P. C., Whitney, P., and Thomas, J. 1999. Visualizing association rules for text mining. In Proceedings of the IEEE Symposium on Information Visualization. 120--123.

Digital Library

[24]

Wong, P. C., Foote, H., Adams, D., Cowley, W., and Thomas, J. 2003. Dynamic visualization of transient data streams. In Proceedings of the IEEE Symposium on Information Visualization. 97--104.

Digital Library

Cited By

Ha HMoon KJoung HKim HLee K(2024)An exploration system to effectively analyze the visual metaphor used in sentiment visualizationInformation Visualization10.1177/1473871624122859323:2(157-196)Online publication date: 15-Feb-2024
https://doi.org/10.1177/14738716241228593
Belavadi PNakayama JCalero Valdez A(2022)Visualizing Large Collections of URLs Using the Hilbert CurveMachine Learning and Knowledge Extraction10.1007/978-3-031-14463-9_18(270-289)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-14463-9_18
Wu YChen ZSun GXie XCao NLiu SCui W(2018)StreamExplorerIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2017.276445924:10(2758-2772)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1109/TVCG.2017.2764459
Show More Cited By

Index Terms

Watch the Story Unfold with TextWheel: Visualization of Large-Scale News Streams
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Document analysis
2. Information systems
  1. Information systems applications

Recommendations

Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts
VINCI '18: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction

The analysis of various opinions and arguments in textual data can be facilitated by automatic topic modeling methods; however, the exploration and interpretation of the resulting topics and terms may prove to be difficult to the analysts. Opinions, ...
Optimizing temporal topic segmentation for intelligent text visualization
IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

We are building a topic-based, interactive visual analytic tool that aids users in analyzing large collections of text. To help users quickly discover content evolution and significant content transitions within a topic over time, here we present a ...
TextFlow: Towards Better Understanding of Evolving Topics in Text

Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 3, Issue 2

February 2012

455 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2089094

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 February 2012

Accepted: 01 October 2010

Revised: 01 October 2010

Received: 01 July 2010

Published in TIST Volume 3, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Research Grants Council, University Grants Committee, Hong Kong

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
876
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)5

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ha HMoon KJoung HKim HLee K(2024)An exploration system to effectively analyze the visual metaphor used in sentiment visualizationInformation Visualization10.1177/1473871624122859323:2(157-196)Online publication date: 15-Feb-2024
https://doi.org/10.1177/14738716241228593
Belavadi PNakayama JCalero Valdez A(2022)Visualizing Large Collections of URLs Using the Hilbert CurveMachine Learning and Knowledge Extraction10.1007/978-3-031-14463-9_18(270-289)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-14463-9_18
Wu YChen ZSun GXie XCao NLiu SCui W(2018)StreamExplorerIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2017.276445924:10(2758-2772)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1109/TVCG.2017.2764459
Carrizosa EGuerrero VHardt DRomero Morales D(2018)On Building Online Visualization Maps for News Data Streams by Means of Mathematical OptimizationBig Data10.1089/big.2018.00176:2(139-158)Online publication date: Jun-2018
https://doi.org/10.1089/big.2018.0017
Kucher KParadis CKerren A(2017)The State of the Art in Sentiment VisualizationComputer Graphics Forum10.1111/cgf.1321737:1(71-96)Online publication date: 12-Jun-2017
https://doi.org/10.1111/cgf.13217
Liu DWeng DLi YBao JZheng YQu HWu Y(2017)SmartAdPIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2016.259843223:1(1-10)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TVCG.2016.2598432
Towne WRosé CHerbsleb J(2016)Measuring Similarity SimilarlyACM Transactions on Intelligent Systems and Technology10.1145/28905108:1(1-28)Online publication date: 26-Sep-2016
https://dl.acm.org/doi/10.1145/2890510
Tanaka SJatowt ATanaka K(2016)Supporting News Article Understanding by Detecting Subject-Background Event Relations2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0044(256-263)Online publication date: Oct-2016
https://doi.org/10.1109/WI.2016.0044
Zheng YWu WChen YQu HNi L(2016)Visual Analytics in Urban Computing: An OverviewIEEE Transactions on Big Data10.1109/TBDATA.2016.25864472:3(276-296)Online publication date: 1-Sep-2016
https://doi.org/10.1109/TBDATA.2016.2586447
Kucher KSchamp-Bjerede TKerren AParadis CSahlgren M(2015)Visual analysis of online social media to open up the investigation of stance phenomenaInformation Visualization10.1177/147387161557507915:2(93-116)Online publication date: 26-Mar-2015
https://doi.org/10.1177/1473871615575079
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents