• Title/Summary/Keyword: 구독자 평점

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Factors Affecting Webtoon's Success: An Empirical Study (웹툰(Webtoon)의 흥행 결정요인 연구)

  • Yang, Ji Hoon;Lee, Ji Young;Lee, Sang Woo
    • The Journal of the Korea Contents Association
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    • v.16 no.5
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    • pp.194-204
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    • 2016
  • With the fast diffusion of smart media, Webtoon has become popular contents among Korean people. Webtoon's content is being used in various content industries, such as movies and drama, and thus its cultural influence is increasing. Using ordinal Regression analysis, this study tried to find major factors affecting webtoon's success. This study found that readers' rating, number of likes, OSMU, author power, genre, picture style are important factors affecting the success of webtoon. This study has several business implications for the Korean webtoon industry.

A Study on the Impact Factors of Contents Diffusion in Youtube using Integrated Content Network Analysis (일반영향요인과 댓글기반 콘텐츠 네트워크 분석을 통합한 유튜브(Youtube)상의 콘텐츠 확산 영향요인 연구)

  • Park, Byung Eun;Lim, Gyoo Gun
    • Journal of Intelligence and Information Systems
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    • v.21 no.3
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    • pp.19-36
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    • 2015
  • Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.