Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Data Mining Approach to Predicting Serial Publication Periods and Mobile Gamification Likelihood for Webtoon Contents

  • Jang, Hyun Seok (BigRadar Co. Ltd.) ;
  • Lee, Kun Chang (SKK Business School/SAIHST (Samsung Advanced Institute for Health Sciences & Technology)/Health Mining Research Center, Sungkyunkwan University)
  • Received : 2018.01.04
  • Accepted : 2018.02.26
  • Published : 2018.04.30
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Abstract

This paper proposes data mining models relevant to the serial publication periods and mobile gamification likelihood of webtoon contents which were either serialized or completed in platform. The size of the cartoon industry including webtoon takes merely 1% of the total entertainment contents industry in Korea. However, the significance of webtoon business is rapidly growing because its intellectual property can be easily used as an effective OSMU (One Source Multi-Use) vehicle for multiple types of contents such as movie, drama, game, and character-related merchandising. We suggested a set of data mining classifiers that are deemed suitable to provide prediction models for serial publication periods and mobile gamification likelihood for the sake of webtoon contents. As a result, the balanced accuracies are respectively recorded as 85.0% and 59.0%, from the two models.

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