Abstract
Mining massive daily news media data to infer patterns of cultural trends, including political conflicts and instabilities, is an important goal of computational social science and the new interdisciplinary field called “culturnomics.” While the sheer size of media data makes this task challenging, a greater hurdle is the nonstationarity of data, manifested in several ways, which invalidates surge in media coverage as a reliable indicator of political change. We demonstrate the use of advanced statistical, information-theoretic, and random fractal methods to analyze CAMEO-encoded political events data. In particular, we show that on the country level, event distributions obey a Zipf-Mandelbrot law, and interactions among countries follow an exponential law, indicating that local or prioritized events dominate the political environment of a country. Most importantly, we find that world-wide political instabilities, such as the Arab Spring, are associated with breakdown or enhancement of long-range correlations in political events.
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References
Michel, J.B., et al.: Quantitative analysis of culture using millions of digitized books. Science 331, 176–182 (2011), doi:10.1126/science.1199644
Leetaru, K.H.: Culturnomics 2.0: Forecasting Large-Scale Human Behavior Using Global News Media Tone in Time And Space. First Monday 16(9) (2011)
Gao, J.B., Hu, J., Mao, X., Perc, M.: Culturomics meets random fractal theory: Insights into long-range correlations of social and natural phenomena over the past two centuries. J. Royal Society Interface (2012), doi:10.1098/rsif.2011.0846
Petersen, A.M., Tenenbaum, J., Havlin, S., Stanley, H.E.: Statistical Laws Governing Fluctuations in Word Use from Word Birth to Word Death. Scientific Reports 2 (2012), doi:10.1038/srep00313.
Goldstein, J.S.: A Conflict-Cooperation Scale for WEIS Events Data. Journal of Conflict Resolution 36, 369–385 (1992)
Schrodt, P.A.: Precedents, Progress and Prospects in Political Event Data. International Interactions 38, 546–569 (2012)
Schrodt, P.A., Gerner, D.J., Ömür, G.: Conflict and Mediation Event Observations (CAMEO): An Event Data Framework for a Post Cold War World. In: Bercovitch, J., Gartner, S. (eds.) International Conflict Mediation: New Approaches and Findings (2009)
O’Brien, S.P.: Crisis Early Warning and Decision Support: Contemporary Approaches and Thoughts on Future Research. International Studies Review 12, 87–104 (2010)
Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison-Wesley (1949)
Mandelbrot, B.: Information Theory and Psycholinguistics. In: Wolman, B.B., Nagel, E. (eds.) Scientific psychology. Basic Books (1965)
Gao, J.B., Cao, Y.H., Tung, W.W., Hu, J.: Multiscale Analysis of Complex Time Series — Integration of Chaos and Random Fractal Theory, and Beyond. Wiley (August 2007)
Gao, J.B., Hu, J., Tung, W.W., Cao, Y.H., Sarshar, N., Roychowdhury, V.P.: Assessment of long range correlation in time series: How to avoid pitfalls. Phys. Rev. E 73, 016117 (2006)
Hu, J., Gao, J., Wang, X.: Multifractal analysis of sunspot time series: the effects of the 11-year cycle and fourier truncation. J. Stat. Mech. P02066 (2009)
Gao, J.B., Hu, J., Tung, W.W.: Facilitating joint chaos and fractal analysis of biosignals through nonlinear adaptive filtering. PLoS One 6(9), e24331 (2011), doi:10.1371/journal.pone.0024331
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Gao, J., Leetaru, K.H., Hu, J., Cioffi-Revilla, C., Schrodt, P. (2013). Massive Media Event Data Analysis to Assess World-Wide Political Conflict and Instability. 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_31
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DOI: https://doi.org/10.1007/978-3-642-37210-0_31
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