Massive Media Event Data Analysis to Assess World-Wide Political Conflict and Instability

  • Jianbo Gao
  • Kalev H. Leetaru
  • Jing Hu
  • Claudio Cioffi-Revilla
  • Philip Schrodt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7812)


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.


Event Data Media Coverage Political Event Hurst Parameter Random Walk Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Michel, J.B., et al.: Quantitative analysis of culture using millions of digitized books. Science 331, 176–182 (2011), doi:10.1126/science.1199644CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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.2011.0846Google Scholar
  4. 4.
    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.Google Scholar
  5. 5.
    Goldstein, J.S.: A Conflict-Cooperation Scale for WEIS Events Data. Journal of Conflict Resolution 36, 369–385 (1992)CrossRefGoogle Scholar
  6. 6.
    Schrodt, P.A.: Precedents, Progress and Prospects in Political Event Data. International Interactions 38, 546–569 (2012)CrossRefGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    O’Brien, S.P.: Crisis Early Warning and Decision Support: Contemporary Approaches and Thoughts on Future Research. International Studies Review 12, 87–104 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison-Wesley (1949)Google Scholar
  10. 10.
    Mandelbrot, B.: Information Theory and Psycholinguistics. In: Wolman, B.B., Nagel, E. (eds.) Scientific psychology. Basic Books (1965)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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.0024331CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jianbo Gao
    • 1
  • Kalev H. Leetaru
    • 2
  • Jing Hu
    • 1
  • Claudio Cioffi-Revilla
    • 3
  • Philip Schrodt
    • 4
  1. 1.PMB Intelligence LLCWest LafayetteUSA
  2. 2.Graduate School of Library and Information ScienceUniversity of IllinoisUrbana-ChampaignUSA
  3. 3.Center for Social Complexity and Department of Computational Social ScienceGeorge Mason UniversityFairfaxUSA
  4. 4.Department of Political SciencePennsylvania State UniversityUSA

Personalised recommendations