• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.576-583
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• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.513-524
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• 2020

Recently, YouTube's growth started drawing attention. YouTube is not only a content-consumption channel but also provides a space for consumers to express their intention. Consumers share their opinions on YouTube through comments. The study focuses on the text of global entrepreneurs' speeches and the comments in response to those speeches on YouTube. A content analysis was conducted for each speech and comment using the text mining software Leximancer. We analyzed the theme of each entrepreneurial speech and derived topics related to the propensity and characteristics of individual entrepreneurs. In the comments, we found the theme of money, work and need to be common regardless of the content of each speech. Talking into account the different lengths of text, we additionally performed a Prominence Index analysis. We derived time, future, better, best, change, life, business, and need as common keywords for speech contents and viewer comments. Users who watched an entrepreneur's speech on YouTube responded equally to the topics of life, time, future, customer needs, and positive change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.355-360
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• 2021

Recently Youtube has been more popular. As many studies show the comparative deprivation of the Social Medeia, this study looks into whether the comparative deprivation is expressed on the YouTube comments. It focuses on the Luxury Haul contents, videos about huge amounts of luxurious products, of which Youtubers'economic feature are demonstrative. The comments of the videos are analyzed with LDA TF-IDF and Word2Vec. Additionally, the comments were classified into positive and negative groups by the LSTM model as well. As a result of the study, even though many comments turned out positive, the negative keywords were indicated related to comparative deprivation. Also it was found that the viewers compared themselves with Youtubers. In particular, some YouTubers are more criticized if they are younger or does not seem to afford the luxurious products themselves. This study suggests that the users express the comparative deprivation on YouTube as well like on the other Social Media.

• Smart Media Journal
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• v.11 no.2
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• pp.18-24
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• 2022

Problems caused by malicious comments occur on many social media. In particular, YouTube, which has a strong character as a medium, is getting more and more harmful from malicious comments due to its easy accessibility using mobile devices. In this paper, we designed and implemented a YouTube malicious comment detection system to identify malicious comments in YouTube contents through LSTM-based natural language processing and to visually display the percentage of malicious comments, such commentors' nicknames and their frequency, and we evaluated the performance of the system. By using a dataset of about 50,000 comments, malicious comments could be detected with an accuracy of about 92%. Therefore, it is expected that this system can solve the social problems caused by malicious comments that many YouTubers faced by automatically generating malicious comments statistics.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.775-778
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• 2021

악성 댓글은 언어폭력이며 사이버 범죄의 일종으로 인터넷상에서 상대방이 올린 글에 비방이나 험담을 하는 악의적인 댓글을 말한다. 악성 댓글을 단순히 차단하는 다른 프로그램들과는 달리 해당 영상의 악성 댓글의 비율을 알려주고 악플러들의 닉네임과 그 빈도를 나타내주는 것으로 차별화를 두었다. 따라서 많은 유튜버들이 겪는 악성 댓글 문제들을 탐지하여 유튜브에 달리는 악성 댓글들을 탐지하고 시각화하여 제공한다.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.10-22
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• 2022

This study analyzed the effects of YouTube comments on belated hit in music chart by focusing on VIDITOR's YouTube channel video called 'BraveGirs_Rollin_comment_Collection'. For this, first of all, it investigated how the comments used by the channel master could achieve storytelling. Moreover, the reason for its popularity hit was researched by analyzing the comments made by users on this video through the 'Tipping point' rules. As a result, it was seen that this video created storytelling by connecting Brave Girls to the story of the Korean army. At the 'Tipping point,' besides, it was revealed that 'the law of the few' is the military, and 'the stickiness factor' is the good image left through Korea's island, 'Baengnyeongdo,' performance. Finally, the rule of 'the power of context' was indicated that it is a difficult reality situation because of COVID-19 and the good situation due to direct communication through the comments of Brave Girls' members and the company.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.1
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• pp.415-442
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• 2021

This study analyzed YouTube beauty videos that focus on makeup for teens, based on the assumption that the main viewers of the videos are teens. Through looking at the interaction between beauty information providers and receivers, communication among information receivers, and the way people participate in comment thread, the study examined how teens engage in social media. Many teens who posted comments on YouTube beauty videos praised and envied the beauty of beauty gurus, and tried to connect with them. The comment thread shows that teens answered to each other's questions on makeup tips, shared thoughts and experiences on issues around teen wearing makeup, which helped them build a sense of community, and broaden their views on the way of life. Also observed was power dynamics among youth, such as lecturing or verbal abuse against children and pre-teens by older teens.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.73-93
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• 2023

This study conducted a comprehensive analysis and evaluation of library services using YouTube through content analysis of YouTube content and emotional response analysis of user comments. This study analyzed 2,169 YouTube contents and 6,487 comments of users from 61 university libraries. The results showed that the number of 'data' content was the largest among 4 categories, followed by 'communication' and 'education' content, and 'promotion' content. Among the sub-classifications, the number of 'information services' contents was the largest. In the analysis of users' emotional responses to YouTube content, the major categories of users' emotional responses were 'data' content and 'communication' content. Most of the user's emotional responses were positive in all categories of content, and the most frequent user emotional expression was 'good'. In addition, the vocabulary used in the user's emotional response was more about the person appearing in the video than the expression of the content of YouTube contents.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.112-120
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• 2021

Korea and Japan share the same view of a single nation state, but there was a difference in the government's COVID-19 emergency relief fund for foreigners. To this end, I analyzed the comments of Korean and Japanese users in YouTube videos related to COVID-19 disaster support funds for foreigners. As a result of the study, As a result, there are many opinions that Korea and Japan are generally sensitive to foreigners' tax payments, and that immigrants can receive national benefits and support like indigenous people if they fulfill their obligations. Next, it can be seen that Korea and Japan recognize each other in common. The difference is that Japan is sensitive to the same nationality, whereas Korea is more focused on tax and money than nationality.