Detecting patterns in North Korean military provocations: What machine-learning tells us

Taehee Whang, Michael Lammbrau, Hyung Min Joo

Research output: Contribution to journalReview articlepeer-review

5 Citations (Scopus)


For the past two decades, North Korea has made a series of military provocations, destabilizing the regional security of East Asia. In particular, Pyongyang has launched several conventional attacks on South Korea. Although these attacks seem unpredictable and random, we attempt in this article to find some patterns in North Korean provocations. To this end, we employ a machine-learning technique to analyze news articles of the Korean Central News Agency (KCNA) from 1997 to 2013. Based on five key words ('years,' 'signed,' 'assembly,' 'June,' and 'Japanese'), our model identifies North Korean provocations with 82% accuracy. Further investigation into these attack words and the contexts in which they appear produces significant insights into the ways in which we can detect North Korean provocations.

Original languageEnglish
Pages (from-to)193-220
Number of pages28
JournalInternational Relations of the Asia-Pacific
Issue number2
Publication statusPublished - 2018

Bibliographical note

Funding Information:
The publication of this article is supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2014S1A5A2A03065042 and NRF-2013S1A3A2055081). Previous versions of this article were presented at the 2014 International Studies Association Annual Convention (Toronto Canada). For questions regarding the article, please contact H. Joo.

Publisher Copyright:
© The Author 2016. Published by Oxford University Press in association with the Japan Association of International Relations; all rights reserved.

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)
  • Political Science and International Relations


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