DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Cha, Meeyoung | - |
dc.contributor.advisor | 차미영 | - |
dc.contributor.author | Kim, Jeongmin | - |
dc.date.accessioned | 2021-05-11T19:34:05Z | - |
dc.date.available | 2021-05-11T19:34:05Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=875460&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/283084 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전산학부, 2019.8,[iii, 19 p. :] | - |
dc.description.abstract | Online live streaming is rapidly growing as the most focused content for internet users. Twitch.tv, which started as a gameplay live broadcast service, is the world’s largest internet broadcast platform with the system for viewers to interact with internet broadcasters through responses such as chatting. In this study, we collected chat data from 2162 videos of 52 channels of Twitch.tv to analyze user reactions appearing in the internet broadcast platform. Based on the user reaction data, we constructed the machine learning based video popularity prediction model and obtained high prediction performances. The user response data collected through this study can be used for future research on internet broadcasting platform and text analysis. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | User response▼alive streaming▼apopularity prediction▼amachine learning | - |
dc.subject | 사용자 반응▼a실시간 방송▼a인기도 예측▼a머신 러닝 | - |
dc.title | Popularity prediction of live streaming content with user response | - |
dc.title.alternative | 사용자 반응을 통한 실시간 방송 콘텐츠 인기도 예측 연구 : Twitch.tv의 사례를 중심으로 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전산학부, | - |
dc.contributor.alternativeauthor | 김정민 | - |
dc.title.subtitle | focusing on the case of twitch.tv | - |
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