• The Journal of Information Systems
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• v.30 no.4
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• pp.173-202
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• 2021

Purpose This study explored overall phenomena in context such as YouTube channel operation, strategy, and profit generation through interviews with two research participants who started their own businesses and are recognized as influencer on YouTube and analysis of viewer responses to uploaded contents. With the explosive growth of YouTube content provision and use, previous studies on YouTube are only being conducted individually on YouTube's content, influence, and content providers, so it is need to explore YouTube channel operations and the effect of revenue generation in context from an integrated perspective. Therefore, the purpose of this study is to present an integrated model that provides a specific process by contextually linking the factors and results influencing YouTube channel operation and revenue generation phenomena to individuals and companies who are trying to operate YouTube channels for the first time. Design/methodology/approach This study systematized and structured the overall phenomena in context such as YouTube channel operation, communication strategy, effect on revenue generation, and YouTube channel operation results by selecting interview subjects and collecting data through interviews, and analyzing viewer reactions (likes, comments, etc.). Due to the lack of previous studies exploring integrated phenomena, research analysis used Strauss & Corbin (1998)'s grounded theory approach, which presented inductive research methods to discover new theories by structuring concepts and categories based on detailed observations and information provided by interviewees. Findings The academic implication of this study is that while previous studies are conducted as individual studies on YouTube's content, influence, and content providers in the current situation where YouTube content provision and use are exploding, it integrally explores and presents an integrated model throughout the process. In addition, taking into account the lack of previous studies, it can be found in the aspect of using the grounded theory approach, an inductive theory approach that establishes a new theory. The practical implications can be found in that it presented practical directions to beginners who want to start operating YouTube channels by identifying operational preparations, communication strategies with viewers, and response management strategies.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.3
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• pp.163-185
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• 2021

In the face of unprecedented pandemics, School libraries should also seek ways to provide non-face-to-face services in an online environment differentiated from existing services, which were mainly limited to face-to-face activities. This study is a basic study for school library services using the operation of YouTube channels. Thourgh the FGI was conducted on teacher librarians of school libraries who are operating YouTube channel, we investigated the awareness, necessity, and specific requirements of school libraries. Furthermore, The second interview to verify the feasibility of our propose ways to activate the operation of school libraries on YouTube in the future.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.109-125
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• 2011

A folksonomy is a new classification approach for organizing information by users to freely attach one or more tags to various resources published on the web. Recently, in order to provide useful services and organize the folksonomy data, many collaborative tagging systems based on folksonomy offer additional functionalities for grouping each elements of a folksonomy. In this paper, organization framework for grouped folksonomy is proposed. That is, we suggest the grouped folksonomy model that is an extended folksonomy with the concept of "group" and fundamental operations(Group Aggregation, Group Composition, Group Intersection, Group Difference) for grouping of folksonomy elements. Also, we developed a supporting tool(GFO) that constructs grouped folksonomy and executes fundamental operations. And we introduce some cases using the fundamental operations for grouping of each elements of folksonomy. Based on suggested our approach, we can construct grouped folksonomy and organize and extract useful information from the folksonomy data by grouping each elements of a folksonomy.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.