Acknowledgement
This research was funded by 「Industrial Strategic Technology Development Program (p0013990, Convergence technology diffusion type Professional Human Resources Development Project)」 of the Ministry of Trade, Industry & Energy (MOTIE, Korea). 본 논문은 2022년 산업통상자원부 및 한국산업기술진흥원(KIAT)의 연구비에 의하여 지원되었음.
References
- J. Y. Park, J. E. Lim & J. S. Jang. (2018). Communication Strategies of YouTube Brand Channel Contents. Korean Advertising and Public Relations Journal, 20(2), 95-15. DOI : 10.16914/kjapr.2018.20.2.95
- S. H. Lee. (2019). The ripple effect and meaning of YouTube. Software Policy Research Institute. https://spri.kr/posts/view/22578?code=industry_trend
- Nasmedia. (2021). Internet usage behavior in 2021. Seoul : Nasmedia.
- Happist. (2021). Internet usage behavior in 2021, YouTube search increase & paid video or paid membership spread. Dreaming island. https://happist.com/579759
- H. J. Seo. (2018). The assessment and prospect of the era of creators. Korea Press Foundation
- J. W. Jeong, J. Y. Lee & C. S. Leem. (2019). An Analysis of Characteristics and User Reactivity by Video Categories on YouTube. Journal of Digital Contents Society, 20(12), 2573-2582. https://doi.org/10.9728/dcs.2019.20.12.2573
- C. A. Madrigal. (2019). The Reason Conspiracy Videos Work So Well on YouTube. The Atlantic. https://www.theatlantic.com/technology/archive/2019/02/reason-conspiracy-videos-work-so-wellyoutube/583282/
- H. S. Kim. (2020). Analysis of Popular YouTube Video Content using Data Mining. Journal of Digital Contents Society, 21(4), 673-681. https://doi.org/10.9728/dcs.2020.21.4.673
- YouTube support. (2022). YouTube's trending videos. YouTube support. https://support.google.com/youtube/answer/7239739?hl=ko
- Turtle Media Strategy Research Center. (2020). How the YouTube Algorithm Works. Turtle Media Strategy Lab. https://gobooki.net/
- X. Cheng, C. Dale & J. Liu. (2008, June). Statistics and social network of youtube videos. In 2008 16th Interntional Workshop on Quality of Service. (pp. 229-238). IEEE.
- M. Bartl. (2018). YouTube channels, uploads and views: A statistical analysis of the past 10 years. Convergence: The International Journal of Research into New Media Technologies, 24(1), 16-32. https://doi.org/10.1177/1354856517736979
- G. Chatzopoulou, C. Sheng & M. Faloutsos. (2010, March). A first step towards understanding popularity in YouTube. In 2010 INFOCOM IEEE Conference on Computer Communications Workshops. (pp. 1-6). San Diego, CA, USA : IEEE.
- E. J. Jeong, H. Y. Cho & M. J. Kang. (2019, May). A Study on the Interesting Factors of Youtube Channel for Generation Z. KSDS Conference Proceeding. (pp. 440-441). Korean Society of Design Science.
- H. J. Byun. (2018). Analyzes the Characteristics in the Contents Production and Usage Environment of YouTube and its Popular Channels; and Examination of its Implications. formative media studies, 21(4), 227-239.
- B. H. Lee. (2016). YouTube history analyzed in terms of revenue -YouTube and Creator media-channel's status to increase YouTube's commercial revenue-. art and media, 15(3), 117-146.
- D. C. An & S. H. Kim. (2012). Attitudes toward SNS Advertising: A Comparison of Blog, Twitter, Facebook, and YouTube. The Korean Journal of Advertising, 23(3), 53-84.
- E. J. Kim & S. C. Whang. (2019). A Study on Advertising Effect Depending on Type of Information Source and Displaying of Economic Support in Influencer Marketing : Focusing on Youtube. Journal of Digital Contents Society, 20(2), 297-306. https://doi.org/10.9728/dcs.2019.20.2.297
- J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi & D. Sampath. (2010, September). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems. (pp. 293-296). Barcelona, Spain : ACM.
- Y. S. Yun & H. U. Lee. (2016. March). Personal Broadcasting Platform Technology: Focusing on Afreeca TV and YouTube. Information and Communications Magazine, 33(4), 56-63.
- S. Y. Yoo & O. R. Jeong. (2015). The YouTube Video Recommendation Algorithm using Users" Social Category. The Korean Institute of Information Scientists and Engineers, 42(5), 664-670.
- J. Ahlquist. (2016). How YouTube is impacting current and future college students. DR. Josie Ahlquist. https://www.josieahlquist.com/2013/10/29/youtubecollege/
- K. S. Kim, H. J. Paek & J. Lynn. (2010). A content analysis of smoking fetish videos on YouTube: regulatory implications for tobacco control. Health communication, 25(2), 97-106. DOI : 10.1080/10410230903544415
- M. Dehghani, M. K. Niaki, I. Ramezani & R. Sali. (2016). Evaluating the influence of YouTube advertising for attraction of young customers. Computers in human behavior, 59, 165-172. DOI : 10.1016/j.chb.2016.01.037
- H. J. Jang. (2021). The Effect of YouTube Content Quality of the Mukbang Channel on Viewing Satisfaction: Focusing on the Moderating Effect of the Food Dietary Lifestyle. Culinary Science & Hospitality Research, 27(11), 206-216. https://doi.org/10.20878/CSHR.2021.27.11.019
- J. W. Lie & J. E. Yang. (2021). The Perception of YouTube Journalism by Entertainment News Producers: An Exploratory Study. Information Society & Media, 22(3), 29-54.
- Y. A. Sung. (2020). Comparative Analysis of Korean-Japan Popular YouTube Content -Based on Social Statistical Approach-. Journal of the Korea Convergence Society, 11(2), 167-174. https://doi.org/10.15207/JKCS.2020.11.2.167
- K. S. Kim. (2019, July). Improvement Method of Classification Rate in ML Antivirus systems using Kaggle Datasets. Proceedings of the Korean Society of Computer Information Conference. (pp.49-52). Seoul : The Korean Society Of Computer And Information.
- B. Jose & S. Abraham. (2017, July). Exploring the merits of nosql: A study based on mongodb. In 2017 International Conference on Networks & Advances in Computational Technologies (NetACT). (pp. 266-271). India Trivandrum : IEEE. DOI: 10.1109/NETACT.2017.8076778
- Minitab. (2022). Interpret the key results for Correlation Coefficient. Minitab. https://support.minitab.com/ko-kr/minitab/18/help-and-how-to/statistics/basic-statistics/how-to/correlation/interpret-the-results/key-results/
- G. Kader & C. Franklin. (2008). The evolution of Pearson's correlation coefficient. The Mathematics Teacher, 102(4), 292-299. DOI : 10.5951/MT.102.4.0292
- J. H. Lee & H. K. Lee. (2017). A Research on Real-time Analysis of Association Rules Using Hash Tags. Internet e-commerce research, 17(4), 105-117.
- Y. K. Yoon. (2016). A comparative study on User Interface Design for SNS Hashtag system. Journal of Korea Institute of Cultural Product & Design, 46, 103-113. DOI : 10.18555/kicpd.2016.46.1
- N. G. Kim, D. H. Lee, H. C. Choi. & W. William Xiu Shun. (2017). Investigations on Techniques and Applications of Text Analytics. Journal of the Korean Telecommunications Society, 42(2), 471-492. DOI : 10.7840/kics.2017.42.2.471
- YouTube support. (2022). Use hashtags to search for videos. YouTube support. https://support.google.com/youtube/answer/6390658?hl=ko
- J. Courtial, (1994). A coword analysis of scientometrics. Scientometrics, 31(3), 251-260. DOI : 10.1007/bf02016875
- I. D. Cho & N. G. Kim. (2011). Recommending Core and Connecting Keywords of Research Area Using Social Network and Data Mining Techniques. Journal of Intelligence and Information Systems, 17(1), 127-138. https://doi.org/10.13088/JIIS.2011.17.1.127
- D. B. Choi, W. S. Choi, S. H. Choi & J. H. Lee. (2020). Perception Survey about SMEs Employment of University Students in Chungbuk Area: Based on Text-mining. Asia Pacific Journal of Small Business, 42(4), 235-250. DOI : 10.36491/APJSB.42.4.9
- J. Y. Lee. (2014). A Comparative Study on the Centrality Measures for Analyzing Research Collaboration Networks. Journal of the Korean Society for Information Management, 31(3), 153-179. DOI : 10.36491/APJSB.42.4.9