Data Mining Approach to Predicting Serial Publication Periods and Mobile Gamification Likelihood for Webtoon Contents

• Published in: 韓國컴퓨터情報學會論文誌 Vol. 23; no. 4; pp. 17 - 24
• Main Authors: Jang, Hyun Seok, Lee, Kun Chang
• Format: Journal Article
• Language: Korean
• Published: 2018
• Subjects: Data Mining Models; Serial Publication Periods; OSMU; Webtoon; Mobile Gamification Likelihood
• ISSN: 1598-849X; 2383-9945

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 <Naver Webtoon> 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.