• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1303-1312
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• 2018

The study analyzed the evaluation of the advertisement effect by the score and the method of the advertisement comments in ad evaluation in online site, 'TVCF'. The results are as follows. First, Internet viral advertisement showed higher number of ad comments and higher evaluation of advertisement effect than cable broadcasting advertisement. Second, the results of analysis of the difference of advertisement evaluation according to ad types and digital user characteristics showed that women are more positive than men toward both cable broadcasting and internet viral advertisement.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.119-133
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• 2020

Recently, as famous YouTubers produce and broadcast videos that receive sponsorship and advertising such as indirect advertising (PPL), a so-called 'back advertising' controversy continues, and not only famous YouTubers but also entertainers are caught up in the issue. It is causing confusion among the public in Korea. This study attempts to find out the public's reaction before and after the controversy of 'back advertising' by YouTubers through comment analysis. Specifically, among text analysis using R programs, we intend to analyze the issue through various methods such as word cloud, qgraph analysis, LDA, and word2vec analysis, a deep learning technique. The target of the analysis was to analyze the channels of three YouTubers who belonged to the controversy of the 'back advertising' YouTuber and uploaded the 'Apology video'. The 5 most recent videos of Muk-bang YouTuber Moon Bok-hee, who has a similar content disposition to SussTV's Han Hye-yeon stylist, which was controversial, and Yang Pang, a YouTuber who showed various contents (August 09, 2020) Criterion and her first 5 videos uploaded were reviewed. As a result of the study, most of the comments that showed positive reactions before the controversy, but after the controversy, it was found that negative reactions accounted for most of the comments. Therefore, this study examines the degree of change of the public about influencers through comments after the controversy over 'back advertising' through various analysis using R program. This research also devises various measures to prevent the occurrence of back advertising of influencers in the future.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.141-144
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• 2009

인터넷에 게시된 글(블로그, 포털 사이트, 게시판 등)에 대한 댓글들 중에서 중복, 광고 등의 스팸성 댓글을 추적하고 이를 필터링 하는 시스템을 개발.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.109-114
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• 2019

This study explored how the tendency of writing 'comment' by Facebook users and the number of 'like' in posting message affected to product attitude, purchasing intention. One hundred thirty five male and female college students were divided into groups with high/low tendency of writing 'comment'. The subjects had to read posting message about athlete shoes on Facebook's newsfeed, different from the conditions under which the 'like' in the posting was high and low. Then, they were responded product attitude and the intention of purchasing. The results of two-way ANOVA showed that the users with low tendency of writing 'comment' displayed more positive product attitude and higher willingness to purchase under condition with a high 'like' number of posting than under condition with a low 'like' number of it.

• 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.21 no.2
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• pp.507-519
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• 2021

This paper attempts to explore advertising factors that affect TikTok advertising effectiveness by identifying motivations to use a short-form video social media platform, TikTok and further looking at perceptions of and attitudes toward TikTok advertising. The results of in-depth interviews with 20s-30s TikTok users suggest that users are motivated to use TikTok for information and fun. Further, TikTok is characterized as a short-form video, rich contents, and a novel format. Regarding TikTok advertising, the results reveal that usefulness, enjoyment, easiness of advertising skip, sense of closeness, and interaction are significant factors of TikTok advertising. Finally, it is suggested that users respond to the advertising by clicking 'like', writing comments, sharing, clicking 'purchase link'/advertiser's website, and creating user-created contents and so on. These findings theoretically contribute to the literature on social media advertising, and practically offer strategic guidelines for TikTok advertising.

• 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.4
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• pp.28-36
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• 2022

This study analyzed 284 K-pop song and dance cover short-form videos recreated by Chinese influencers uploaded on TikTok, to explore which reform factors of image similarity, language similarity, the extent of audience participation leading, the extent of lyrics or subtitles translated into Chinese, PPL disclosure, the length of video and the reputation of influencer affected Chinese TikTok audiences' reactions - number of "Likes," "Comments" and "Shares." The results showed that only the "reputation of influencer" was significantly affected the number of "Likes" which estimated as a relatively passive response, but the other factors affected the number of "Comments" and "Shares" significantly which estimated as more active responses. The more an influencer is perceived as not similar to the singer in terms of image the more comments were posted. And the videos expressed in Korean archived more comments and shares than those lyrics or subtitles translated into Chinese. This study is meaningful in that it confirmed the necessity of influencers in the globe diffusion of K-pop, by specifically analyzing the audience's reactions according to the characteristics of UCCs created by local influencers using short-form video platforms.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.435-444
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• 2022

There are three focuses in the paper. Firstly, the study identified what channels were most viewed by YouTube users to watch the 'Daejang-dong scandal,' which was the most powerful agenda to influence the candidate preference among voters during the 20th presidential election. Secondly, the study analyzed whether the political tone of the first videos was in line with that of the subsequent videos. Finally, we compared the sentiment of comments on the first and subsequent videos. The results showed that TBS 'News Factory' and 'TV Chosun News' represented liberal and conservative factions, respectively. Secondly, the political tone of channels that were viewed subsequently was neutral, but the conservative channel users left more negative comments and that was significant statistically. In addition, about 80% of the conservative and liberal channel users shared the same political tendency with the channel they watched first, and more than 90% of the comments left at the subsequent videos in line with that of at the first news. Based on these results, the study concluded that the voters tended to seek political news that was similar with their political ideology, and it was considered a sort of echo chamber phenomenon on the YouTube. The study suggests that the performance of high-quality journalism by traditional news outlet might contribute to decrease the negative influence of political contents on YouTube users.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1087-1101
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• 2013

Most people are connected to Social Network Services (SNS) through smart devices. Social Network Services are tools that transport information fast and easily. It does not care where he or she comes from. A lot of information circulates and is shared on Social Network Services. but Social Network Services faults are magnified and becoming a serious issue. For instance, malicious users generate multiple IDs easily on Facebook and he can use personal information of others on purpose, because most people tend to undoubtedly accept friend requests. In this paper, we have specified research scope to Facebook, which is one of most popular Social Network Services in the world. We propose a way of minimizing the number of malicious actions on Facebook from malignant users and malicious bots by setting criteria and applying reputation system.