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International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation

SBP-BRiMS 2016: Social, Cultural, and Behavioral Modeling pp 282–290Cite as

Identifying Political “hot” Spots Through Massive Media Data Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9708))

Abstract

Political processes generated by daily events around the world are highly complicated. Using an information theoretic approach, we examine the dissimilarity of political activities of a country to different countries, and whether it is possible to identify politically “hot” countries. Using a massive political science data, the Global Database of Events, Location, and Tone (GDELT), which covers all the political event data (over 300 million) since 1979 created for studying world-wide political conflict and instability, we show that Shannon entropy for most countries does not differ substantially. Therefore, most countries have similar complexity in terms of political activities. More interestingly, we find that the relative entropies between politically very unstable countries and regular stable countries are large, and thus relative entropy can be used to effectively identify political “hot” spots.

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Author information

Authors and Affiliations

  1. Institute of Complexity Science and Big Data Technology, Guangxi University, 100 Daxue Road, Nanning, Guangxi, 530005, People’s Republic of China

    Peng Fang, Jianbo Gao, Fangli Fan & Luhai Yang

  2. PMB Intelligence LLC, Sunnyvale, CA, USA

    Jianbo Gao

Authors
  1. Peng Fang
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  2. Jianbo Gao
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  3. Fangli Fan
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  4. Luhai Yang
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Corresponding authors

Correspondence to Peng Fang or Jianbo Gao .

Editor information

Editors and Affiliations

  1. University of Toledo, Toledo, Ohio, USA

    Kevin S. Xu

  2. Penn State University, University Park, Pennsylvania, USA

    David Reitter

  3. Penn State University, University Park, Pennsylvania, USA

    Dongwon Lee

  4. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Nathaniel Osgood

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© 2016 Springer International Publishing Switzerland

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Fang, P., Gao, J., Fan, F., Yang, L. (2016). Identifying Political “hot” Spots Through Massive Media Data Analysis. In: Xu, K., Reitter, D., Lee, D., Osgood, N. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science(), vol 9708. Springer, Cham. https://doi.org/10.1007/978-3-319-39931-7_27

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  • DOI: https://doi.org/10.1007/978-3-319-39931-7_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39930-0

  • Online ISBN: 978-3-319-39931-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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