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Detecting patterns in North Korean military provocations

What machine-learning tells us

Bibliographic Data

ID12701111
AuthorsTaehee Whang (0000-0002-2573-3740, Yonsei University), Michael Lammbrau (Mercyhurst University), Hyung-Min Joo (0000-0002-7138-9442, Korea University, corresponding author)
Year2016
Pageslcw016-lcw016
Publication date2016-10-19
Open AccessNo
TypeARTICLE
VenueInternational Relations of the Asia-Pacific (JOURNAL)
Journal identifiersISSN: 1470-482X • E-ISSN: 1470-4838
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.1093/irap/lcw016
OpenAlexW2537236828
LanguageEN
Citations received5
References cited7

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

Agency (philosophy · China · Computer security · East Asia · Geography · Key (lock · Political science · Social science · Sociology · Computational and Text Analysis Methods · Computer Science · Korean Peninsula Historical and Political Studies · Technology and Data Analysis · History · Law

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Citations per year0.56
Citation span2017 - 2026 (10)
Highly citedNo

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