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)

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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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

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