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Visual Analytics using Topic Composition for Predicting Event Flow
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 Title & Authors
Visual Analytics using Topic Composition for Predicting Event Flow
Yeon, Hanbyul; Kim, Seokyeon; Jang, Yun;
Emergence events are the cause of much economic damage. In order to minimize the damage that these events cause, it must be possible to predict what will happen in the future. Accordingly, many researchers have focused on real-time monitoring, detecting events, and investigating events. In addition, there have also been many studies on predictive analysis for forecasting of future trends. However, most studies provide future tendency per event without contextual compositive analysis. In this paper, we present a predictive visual analytics system using topic composition to provide future trends per event. We first extract abnormal topics from social media data to find interesting and unexpected events. We then search for similar emergence patterns in the past. Relevant topics in the past are provided by news media data. Finally, the user combines the relevant topics and a new context is created for contextual prediction. In a case study, we demonstrate our visual analytics system with two different cases and validate our system with possible predictive story lines.
predictive analysis;visual analytics;social media data analysis;abnormal event detection;
 Cited by
J. Chae, D. Thom, H. Bosch, Y. Jang, R. Maciejewski, D. S. Ebert, and T. Ertl, "Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition," IEEE Conference on Visual Analytics Science and Technology, VAST, 2012.

Hanbyul Yeon, Yun Jang, "Visual Analytics for Abnormal Event detection using Seasonal-Trend Decomposition and Serial-Correlation," Journal of KIISE, Vol. 41, No. 12, pp. 1066-1074, 2014. crossref(new window)

T. Sakaki, M. Okazaki, and Y. Matsuo, "Earthquake shakes twitter users: Real-time event detection by social sensors," Proc. of the 19th International Conference on World Wide Web, WWW'10, 2010.

F. Wanner, A. Stoffel, D. Jackle, B. C. Kwon, A. Weiler, and D. A. Keim, "State-of-the-Art Report of Visual Analysis for Event Detection in Text Data Streams," Proc. of Eurographics Conference on Visualization (EuroVis), 2014.

Bosch H., Thom D., Heimerl F., Puttmann E., Koch S., Kruger R., Worner M., Ertl T., "Scatterblogs2: Real-time monitoring of microblog messages through user-guided filtering," Journal of IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 12, 2013.

H. Sayyadi, M. Hurst, and A. Maykov, "Event detection and tracking in social streams," Proc. of the International Conference on Weblogs and Social Media, 2009.

T. Takahashi, R. Tomioka, and K. Yamanishi, "Discovering emerging topics in social streams via link-anomaly detection," IEEE Trans. Knowl. Data Eng., 2014.

H. Becker, "Identification and Characterization of Events in Social Media," PhD thesis, Columbia University, 2011.

J. Bollen, H. Mao, and X. Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, 2011.

E. T. K. Sang and J. Bos, "Predicting the 2011 dutch senate election results with twitter," Proc. of the Workshop on Semantic Analysis in Social Media, 2012.

A. Tumasjan, T. Sprenger, P. Sandner, and I. Welpe, "Predicting elections with twitter: What 140 characters reveal about political sentiment," Proc. of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 178-185, 2010.

M. El Assady, W. Jentner, M. Stein, F. Fischer, T. Schreck, and D. A. Keim, "Predictive Visual Analytics Approaches for Movie Ratings and Discussion of Open Research Challenges," Proc. of the IEEE VIS 2014 Workshop Visualization for Predictive Analytics, 2014.

M. C. Hao, H. Janetzko, S. Mittelstadt, W. Hill, U. Dayal, D. A. Keim, M. Marwah, and R. K. Sharma, "A visual analytics approach for peak-preserving prediction of large seasonal time series," Comput. Graph. Forum, 30(3):691-700, 2011. crossref(new window)

Y. Lu, R. Kruger, D. Thom, F. Wang, S. Koch, T. Ertl, and R. Maciejewski, "Integrating predictive analytics and social media," Proc. Visual Analytics Science and Technology (VAST), 2014 IEEE Conference, 2014.