Abstract
Twitter is a microblogging website that has been useful as a source for human social behavioral analysis, such as political sentiment analysis, user influence, and spread of news. In this paper, we discuss a text cube approach to studying different kinds of human, social and cultural behavior (HSCB) embedded in the Twitter stream. Text cube is a new way to organize data (e.g., Twitter text) in multiple dimensions and multiple hierarchies for efficient information query and visualization. With the HSCB measures defined in a cube, users are able to view statistical reports and perform online analytical processing. Along with viewing and analyzing Twitter text using cubes and charts, we have also added the capability to display the contents of the cube on a heat map. The degree of opacity is directly proportional to the value of the behavioral, social or cultural measure. This kind of map allows the analyst to focus attention on hotspots of concern in a region of interest. In addition, the text cube architecture supports the development of data mining models using the data taken from cubes. We provide several case studies to illustrate the text cube approach, including public sentiment in a U.S. city and political sentiment in the Arab Spring.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Numrich, S.K., Tolk, A.: Challenges for Human, Social, Cultural, and Behavioral Modeling. SCS M&S Magazine 1(1) (January 2010)
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: What 140 characters reveal about political sentiment. In: International AAAI Conference on Weblogs and Social Media (2010)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring User Influence in Twitter: The Million Follower Fallacy. In: Fourth International AAAI Conference on Weblogs and Social Media (2010)
Lerman, K., Ghosh, R.: Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks. In: Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC, May 23-26 (2010)
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-Tab, and Sub Totals. Data Mining and Knowledge Discovery 1(1), 29–53 (1997)
Liu, X., Tang, K., Hancock, J., Han, J., Song, M., Xu, R., Manikonda, V., Pokorny, B.: SocialCube: A Text Cube Framework for Analyzing Social Media Data. In: Proceedings of ASE International Conference on Social Informatics, Washington, DC (December 2012)
Lin, C., Ding, B., Han, J., Zhu, F., Zhao, B.: Text Cube: Computing IR Measures for Multidimensional Text Database Analysis. In: Proc. 2008 Int. Conf. on Data Mining, Pisa, Italy (December 2008)
Zhang, D., Zhai, C., Han, J.: Topic Cube: Topic Modeling for OLAP on Multidimensional Text Databases. In: Proc. 2009 SIAM Int. Conf. on Data Mining, Sparks, NV (April 2009)
Zhang, D., Zhai, C., Han, J.: MiTexCube: MicroTextCluster Cube for Online Analysis of Text Cells. In: Proc. 2011 NASA Conf. on Intelligent Data Understanding, Mountain View, CA (October 2011)
Zhao, B., Lin, C.X., Ding, B., Han, J.: TEXplorer: Keyword based object ranking and exploration in multidimensional text databases. In: Int. Conf. on Information and Knowledge Management (October 2011)
Liu, X., Tang, K., Buhrman, J.R., Cheng, H.: An agent-based framework for collaborative data mining optimization. In: IEEE International Symposium on Collaborative Technologies and Systems (2010)
Tang, K., Liu, X., Tang, Y., Manikonda, V., Buhrman, J.R., Cheng, H.: ABMiner: A scalable data mining framework to support human performance analysis. In: International Conference on Applied Human Factors and Ergonomics (July 2010)
Brown, C., Frazee, J., Beaver, D., Liu, X., Hoyt, F., Hancock, J.: Evolution of Sentiment in the Libyan Revolution (2011), White Paper at https://webspace.utexas.edu/dib97/libya-report-10-30-11.pdf
Liu, X., Hancock, J., Zhang, G., Xu, R., Bazarova, N.: Exploring linguistic features for deception detection in unstructured text. In: Hawaii International Conference on System Sciences, January 4-7 (2012)
Ekman, P.: Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage. Norton & Company Inc., New York (2001)
Russell, J.A.: A circumplex model of affect. Journal of Personality and Social Psychology 39, 1161–1178 (1980)
Mehrabian, A.: Nonverbal communication. Aldine-Atherton, Chicago (1972)
Hancock, J.T., Landrigan, C., Silver, C.: Expressing emotion in text. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2007), pp. 929–932 (2007)
Hancock, J.T., Gee, K., Ciaciaco, K., Mae, J.: I’m sad you’re sad: Emotional contagion in CMC. In: Proceedings of the ACM Conference on Computer-Supported Cooperative Work (2008)
Kramer, A.D.I.: An unobtrusive behavioral model of “Gross National Happiness”. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (2010)
Golder, S., Macy, M.: Diurnal and Seasonal Mood Vary with Work, Sleep and Daylength across Diverse Cultures. Science 333, 1878–1881 (2011)
Pennebaker, J.W., Booth, R.J., Francis, M.E.: Linguistic Inquiry and Word Count: LIWC. LIWC, Austin, http://www.liwc.net
Schwarz, N., Clore, G.L.: Mood, Misattribution, and Judgments of Well-Being: Informative and Directive Functions of Affective States. JPSP 45, 513–523 (1983)
Fan, R.-E., Chen, P.-H., Lin, C.-J.: Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6, 1889–1918 (2005)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
http://en.wikipedia.org/wiki/Timeline_of_the_2011-2012_Egyptian_revolution
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, X. et al. (2013). A Text Cube Approach to Human, Social and Cultural Behavior in the Twitter Stream. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-37210-0_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37209-4
Online ISBN: 978-3-642-37210-0
eBook Packages: Computer ScienceComputer Science (R0)