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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings
Pages284-292
Number of pages9
DOIs
StatePublished - Mar 14 2013
Event6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013 - Washington, DC, United States
Duration: Apr 2 2013Apr 5 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7812 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013
CountryUnited States
CityWashington, DC
Period4/2/134/5/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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