The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - May-June (2013 vol.15)
pp: 72-82
Gennady Andrienko , Fraunhofer Institute for Intelligent Analysis and Information Systems
Natalia Andrienko , Fraunhofer Institute for Intelligent Analysis and Information Systems
Harald Bosch , University of Stuttgart
Thomas Ertl , University of Stuttgart
Georg Fuchs , Fraunhofer Institute for Intelligent Analysis and Information Systems
Piotr Jankowski , San Diego State University
Dennis Thom , University of Stuttgart
ABSTRACT
An exploratory study of the potential of georeferenced Twitter data (using tweets from Seattle-area residents over a two-month period) extracts knowledge about people's everyday life.
INDEX TERMS
Analytical models, Scientific computing, Visual analytics, Twitter, Social network services, Geophysical measurements, scientific computing, georeferencing, visual analytics
CITATION
Gennady Andrienko, Natalia Andrienko, Harald Bosch, Thomas Ertl, Georg Fuchs, Piotr Jankowski, Dennis Thom, "Thematic Patterns in Georeferenced Tweets through Space-Time Visual Analytics", Computing in Science & Engineering, vol.15, no. 3, pp. 72-82, May-June 2013, doi:10.1109/MCSE.2013.70
REFERENCES
1. J. Wang et al., “Using Service-Based GIS to Support Earthquake Research and Disaster Response,” Computing in Science & Eng., 2012, vol. 14, no. 5, pp. 21–30.
2. A. Java et al., “Why We Twitter: Understanding Microblogging Usage and Communities,” Proc. 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, ACM, 2007, pp. 56–65.
3. J. Chae et al., “Spatiotemporal Social Media Analytics for Abnormal Event Detection Using Seasonal-Trend Decomposition,” Proc. IEEE Conf. Visual Analytics Science and Technology (VAST), IEEE CS, 2012, pp. 143–152.
4. H. Saif, Y. He, and H. Alani, “Alleviating Data Sparsity for Twitter Sentiment Analysis,” Proc. Making Sense of Microposts (MSM2012), vol. 838, paper 1, 2012; http://ceur-ws.org/Vol-838paper_01.pdf.
5. S. Carter, M. Tsagkias, and W. Weerkamp, “Twitter Hashtags: Joint Translation and Clustering,” Proc. ACM Web Science, ACM 2011, pp. 1–3.
6. K. Gimpel et al., “Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments,” Proc. Human Language Technologies, Assoc. for Computational Linguistics (ACL), 2011, pp. 42–47.
7. X. Liu et al., “Recognizing Named Entities in Tweets,” Proc. Human Language Technologies, ACL, 2011, pp. 359–367.
8. D. Thom et al., “Spatiotemporal Anomaly Detection through Visual Analysis of Geolocated Twitter Messages,” Proc. IEEE Pacific Visualization Symp., IEEE CS, 2012, pp. 41–48.
9. N. Andrienko and G. Andrienko, “Spatial Generalization and Aggregation of Massive Movement Data,” IEEE Trans. Visualization and Computer Graphics, 2011, vol. 17, no. 2, pp. 205–219.
10. G. Andrienko et al., “Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data,” Proc. IEEE Visual Analytics Science and Technology, IEEE, 2011, pp. 161–170.
11. D. Thom, H. Bosch, and T. Ertl, “Inverse Document Density: A Smooth Measure for Location-Dependent Term Irregularities,” Proc. Int'l. Conf. Computational Linguistics, ACL, 2012, pp. 2603–2618.
12. G. Andrienko et al., “Scalable Analysis of Movement Data for Extracting and Exploring Significant Places,” IEEE Trans. Visualization and Computer Graphics, 2013, vol. 19,accepted for publication.
13. G. Andrienko and N. Andrienko, “Privacy Issues in Geospatial Visual Analytics,” Proc. 8th Symp. Location-Based Services (LBS), Springer, 2011, pp. 239–246.
52 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool