Summarizing Dynamic Bipolar Conflict Structures
November/December 2006 (vol. 12 no. 6)
pp. 1486-1499
DOI Bookmark:
http://doi.ieeecomputersociety.org/10.1109/TVCG.2006.105
Abstract—We present a method for visual summary of bilateral conflict structures embodied in event data. Such data consists of actors linked by time-stamped events, and may be extracted from various sources such as news reports and dossiers. When analyzing political events, it is of particular importance to be able to recognize conflicts and actors involved in them. By projecting actors into a conflict space, we are able to highlight the main opponents in a series of tens of thousands of events, and provide a graphic overview of the conflict structure. Moreover, our method allows for smooth animation of the dynamics of a conflict. [1] J. Allan, R. Gupta, and V. Khandelwal, “Temporal Summaries of News Topics,” Proc. ACM-SIGIR '01, pp. 10-18, 2001.[2] J. Allan, R. Papka, and V. Lavrenko, “On-Line New Event Detection and Tracking,” Proc. ACM-SIGIR '98, pp. 37-45, 1998.[3] C. Best, E. Van der Groot, and M. de Paola, “Thematic Indicators Derived from World News Reports,” Proc. Int'l Conf. Intelligence and Security Informatics (ISI '05), pp. 436-447, 2005.[4] Network Analysis—Methodological Foundations, U. Brandes and T. Erlebach, eds. Springer-Verlag, 2005.[5] U. Brandes, D. Fleischer, and J. Lerner, “Highlighting Conflict Dynamics in Event Data,” Proc. IEEE Symp. Information Visualization (InfoVis '05), pp. 103-110, 2005.[6] U. Brandes and J. Lerner, “Structural Similarity in Graphs,” Proc. 15th Int'l Symp. Algorithms and Computation (ISAAC '04), pp. 184-195, 2004.[7] J.M. Chambers, W.S. Cleveland, B. Kleiner, and P.A. Tukey, Graphical Methods for Data Analysis. Wadsworth, 1983.[8] W.S. Cleveland and R. McGill, “The Many Faces of a Scatterplot,” J. Am. Statistical Assoc., vol. 79, no. 388, pp. 807-822, 1984.[9] J.S. Goldstein, “A Conflict-Cooperation Scale for WEIS International Events Data,” J. Conflict Resolution, vol. 36, no. 2, pp. 369-385, 1992.[10] G.H. Golub and C.F. van Loan, Matrix Computations. John Hopkins Univ. Press, 1996.[11] V.M. Hudson, P.A. Schrodt, and R.D. Whitmer, “A New Kind of Social Science: The Path beyond Current (IR) Methodologies May Lie beneath Them,” Proc. Ann. Int'l Studies Assoc. Conf., 2004.[12] Drawing Graphs, M. Kaufmann and D. Wagner, eds. Springer-Verlag, 2001.[13] M. Krivelevich and V.H. Vu, “On the Concentration of Eigenvalues of Random Symmetric Matrices,” Technical Report MSR-TR-2000-60, Microsoft Research, 2000.[14] C.A. McClelland, World Event/Interaction Survey Codebook (icpsr 5211), 1976.[15] P.A. Schrodt, S.G. Davis, and J.L. Weddle, “Political Science: KEDS—A Program for the Machine Coding of Event Data,” Social Science Computer Rev., vol. 12, no. 3, pp. 561-588, 1994.[16] B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” Proc. IEEE Visual Languages Conf., 1996.[17] G.W. Stewart and J.-G. Sun, Matrix Perturbation Theory. Academic Press, 1990.[18] T. Widmer and V. Tröger, “Event Data Based Network Analysis,” Proc. 45th Ann. Convention of the Int'l Studies Assoc., 2004.[19] P.C. Wong, H. Foote, D. Adams, W. Cowley, and J. Thomas, “Dynamic Visualization of Transient Data Streams,” Proc. IEEE Symp. Information Visualization (InfoVis '03), pp. 97-104, 2003.[20] K. Yamaguchi, Event History Analysis. Sage, 1991.
Index Terms:
Information visualization, text mining, event analysis, time-dependent visualization.
Citation:
Ulrik Brandes, Daniel Fleischer, J? Lerner, "Summarizing Dynamic Bipolar Conflict Structures," IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 6, pp. 1486-1499, Nov./Dec. 2006, doi:10.1109/TVCG.2006.105
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