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

Author
Taehee Whang, Michael Lammbrau, Hyung-min Joo
Content Type
Journal Article
Journal
International Relations of the Asia-Pacific
Volume
18
Issue Number
2
Publication Date
May 2018
Institution
Japan Association of International Relations
Abstract
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.
Topic
Security, Military Strategy, Military Affairs
Political Geography
Asia, South Korea, North Korea, Asia-Pacific