Jianbo Gao and Claudio Cioffi-Revilla, University of Florida, USA and George Mason University, USA

Massive media event data analysis to assess world-wide political conflict and instability
Mining massive media data to gain insights into various kinds of cultural trends, including political conflicts and instabilities, are a primary goal of computational social science and the new interdisciplinary field, "culturomics". 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.