• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.389-397
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• 2019

As the usage of e-cigarettes and its effects on health harms receive attentions, the debate over regulating e-cigarette industry's' social media marketing has been heated. While the industry has aggressively employed social media marketing such as YouTube, little research has examined what kinds of e-cigarette information is delivered to Korean viewers through YouTube. This study investigated the presence of product-related information and health harm information in the videos, and the characteristics of YouTube creators. The proportion of female creators was smaller than male creators, but more than female smoking rates. The results showed that product-related information such as taste, design and convenience was mentioned more than half the videos, but health ham was mentioned less. Videos which mentioned convenience and design tended to not mention health harm. These results could be used to support the current regulation approach over e-cigarette YouTube marketing.

• Journal of the Korean Chemical Society
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• v.54 no.4
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• pp.465-470
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• 2010

Using interesting video clips corresponding to lesson subjects for students who favour visual representation is one of the good methods to enhance students' preference for science class. There are many moving picture web sites to get video clips easily via internet and 'YouTube' is very popular and one of the largest reservoir. In this study, every student in the 'Chemistry I' class, which is a class for 11th grade, was requested to search a video clip corresponding to lesson subjects and to make a presentation in the class. After 1st semester, students' response about the class using YouTube was examined by survey. As a result, students preferred and were interested in the class using YouTube than class centered on textbook. And students preferred YouTube clips showing unusual experiments that were related with contents of subject. In addition, experiments and watching their real phenomena were an interesting factor and helpful factor of learning chemistry in YouTube video clips, respectively. However, translation of English used in the video clips seemed to be a difficult part for students.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.29-42
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• 2020

The purpose of this study is to present a qualitative research method for extracting and analyzing the comments written by YouTube video users. To do this, we used YouTube users' feedback to measure the hedonic, social, and utilitarian gratification of use and gratification theory(UGT) through by using analysis and topic modeling. The result of the measurement found that the first reason why users watch the trot singer, Cho Myung-sub's video in the KBS Korean broadcasting channel is to achieve hedonic gratification with high frequency. In word-document network analysis, the degree of centrality was high in words, such as 'cheering', 'thank you', 'fighting', and 'best'. Betweenness centrality is similar to the degree of centrality. Eigenvector centrality also shows that words such as 'love', 'heart', and 'thank you' are the most influential words of users' opinions. The results of the centrality analysis present that the majority of video users show their 'love', 'heart' and 'thank you' for the video. it indicates that the high words in centrality analysis is consistent with the high frequency words of hedonic and social gratification dimension of the UGT. The study has research methodological implication that shed light on the motivations for watching YouTube videos with UGT using text mining techniques that automate qualitative analysis, rather than following a survey-based structural equation model.

• Journal of KIISE
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• v.42 no.5
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• pp.664-670
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• 2015

With the rapid progression of the Internet and smartphones, YouTube has grown significantly as a social media sharing site and has become popular all around the world. As users share videos through YouTube, social data are created and users look for video recommendations related to their interests. In this paper, we extract users' social category based on their social relationship and social category classification list using YouTube data. We propose the YouTube recommendation algorithm using the extracted users' social category for more accurate and meaningful recommendations. We show experiment results of its validation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.209-211
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• 2022

YouTube, a video platform used by millions of people worldwide, provides a rapidly growing video service. This study aims to understand the characteristics and cultural differences of each country using the Kaggle dataset, one of the public datasets, and to show the usefulness of the public dataset. For this purpose, we analyze data from 11 countries, 15 categories, and about 1.1 million trending videos. This study adopts Python to obtain the number of videos by category for data analysis, the selection period of videos rapidly increasing in popularity, and the ratio of unique videos. In the future, based on machine learning, we plan to research to help diagnose individual videos and establish channel operation plans and strategies by predicting the selection possibility and selection period based on machine learning.

• Journal of Science Education
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• v.45 no.1
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• pp.118-128
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• 2021

In this study, as the viewing characteristics of users of YouTube video content related to science education, 'Inflow and Access' is analyzed to find out the interaction between learners and the system, and 'Reaction and Subscription' to find out the interaction between learners and contents. To this end, the YouTube channel "Elementary Science TV," was selected as the subject of research. The channel is mainly focused on the contents of elementary science textbooks, STEAM, and gifted education. The channel's data of YouTube studio was analyzed. The following results were obtained through data analysis: first, as a result of 'Inflow and Access' analysis, YouTube video content related to science education was most often introduced through external links, and the access device was mainly a computer. Second, as a result of the analysis of 'Reaction and Subscription,' 'like' and commenting performed as a reaction to the video were less than 1% of the number of views. Most users watch without a subscription, and watch for longer when using self-directed. Although this study was analyzed through a limited channel called 'Elementary Science TV,' we were able to discover a little about the users' viewing characteristics of YouTube video contents related to science education. In the future, it is expected that it can be used as a basic material for creating videos related to science education for remote classes, establishing a science education video platform.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.11-17
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• 2018

This study analyzes the characteristics of popular channels of YouTube created in South Korea. As the science and technology have developed, the ordinary people come to upload their own videos after filming them. Regarding YouTube, the most popular video provide service in the world, this study researches the traits of popular YouTube channels produced in South Korea. Specifically, top 100 channels of 1) Most Subscribed 2) Most Viewed are researched, then it explores further by classifying them as 1) The number of the most subscribed/the most viewed 2) The number of videos 3) The number of viewed/subscribed 4) Genre 5) Type of creator. Thus, it not only reveals the multi-dimensional aspects of popular YouTube channels in South Korea but also prospects the market of Multi-Channel Network(MCN).

• Journal of Korean Society of Archives and Records Management
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• v.22 no.4
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• pp.45-65
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• 2022

YouTube videos of public institutions are digital public records that need to be managed and preserved. As such, the video and the metadata describing the video should also be preserved. This study aims to select the key metadata elements necessary for archiving videos published on YouTube by public institutions. To this end, five high-level areas, namely the description, structure, management, preservation, and user participation, and the metadata elements of 10 subareas, were designed by referring to NAK 8, PREMIS, ISAD(G), and YouTube metadata. Afterward, the metadata elements designed by 14 experts were verified. Lastly, the validity and reliability of the evaluation results were verified. Of the 63 elements, 33 satisfied the validity and reliability criteria. Thus, these elements were selected as the core metadata for archiving YouTube videos in public institutions.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.179-180
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• 2018

최근 N스크린 세대의 새로운 소비 주도층인 밀레니얼세대(10대~20대 초반)의 증가로 모바일 YouTube 동영상 시청자가 증가하였다. 이로 인해 광고 노출 가능성이 높아지고 있어 YouTube 인스트림 광고가 광고주들의 높은 주목을 받고 있다. 그러나 현재 광고 게재에 따른 정확한 노출 효과에 대한 연구 분석 자료는 현저히 부족하다. 이에 모바일 YouTube 동영상 시청자 수가 가장 높은 10대~20대 초반을 대상으로 국내 YouTube 인스트림 광고 사례 10개를 선정하여 선택적 시청에 따른 시청자들의 행태 파악과 광고 몰입도를 분석해 보았다. 분석 결과 소구 유형별 광고 몰입도는 이성소구보다 감성소구가 컸으며, 그 중 유머소구의 광고 몰입도가 가장 크게 나타났다. 본 연구 결과가 향후 광고 노출 효과를 높이는데 도움이 되기를 기대한다.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.