A Big Data Analysis of K-POP on Social Media: focused on Images, Figures, and Public Attitude

Year 2022, Volume: 10 Issue: 2, 229 - 241, 30.06.2022

Johee Lee Inho Lee

https://doi.org/10.12975/rastmd.20221024

Abstract

This study aims to present the image and thoughts about K-POP among the public through objective data as we realized that it is necessary to conduct a systematic analysis of the public mentality and attitude toward K-POP to sustain the movement of Hallyu or Korean Wave. To this end, we used the big data in 2014 and 2021 to compare and analyze the K-POP-related images, figures, and public attitude between the two time points. The result can be summarized as follows: First, between 2014 and 2021, the changes over time in the media, where K-POP can be accessed by the audience, are more pronounced than common images about K-POP; Second, the figures that appeared consistently and commonly in 2014 and 2021 were found to be EXO and BTS, popular South Korean boy-bands. In the case of EXO, there was no commonly related figure at both points of time, but as for BTS, it was found that their fan club, A.R.M.Y, appeared as a common related figure; Third, compared to 2014, the rate of positive response toward K-POP decreased while negative response increased in 2021; in other words, the attitude toward K-POP turned negative. Unlike the previous studies that collect data with questionnaires or materials and analyze them, we used the already stored data and Sometrend, Korea’s big data analysis software, provided by VAIV Company, to find out what people think about K-POP. This study is significant as it extracts information from the existing data, analyzes the contents, and reinterprets the results from various angles to derive meaningful results. It is expected that the findings of this study, presented through precise data—the K-POP-related images, figures, and public attitude—will be used as basic material to contribute to the sustaining of the K-POP wave.

Keywords

K-POP, Big Data, Social Media, BTS, EXO

References

• Kim, Y.(2011). Social Network Analysis, Seoul: PAKYOUNGSA, 5.
• Cho, B., & Sim, H. (2013). Success Factor Analysis of K-POP and A Study on sustainable Korean Wave, The Korea Contents Society.
• KIM, E. (2015). Analysis on the Musical Value and Sustainable Growth of K-pop, KOREA ASSOCIATION of GLOBAL CULTURE.
• Hwang, H. (2013). A Discussion on the Expansion of K-POP Music to the Global Pop Music Industry Through the Sensation of Korean Singer, PSY, Journal of Music Education Science.
• Lee, I., & Kwon, S. (2021). Beyond K-POP Paradigm: Value Creation driven by Big Hit Entertainment’s Korean Culture Innovation, Korea Business Review.
• Lee, S., & Jang, M. (2019). Analysis of global success factors of K-pop music, The Korea Entertainment Industry Association.
• O’Reilly Radar Team. (2012). Planning for Big Data. Newton: O’Reilly.
• Google. (2010). Eric Schmidt at Technomy. http://www.youtube.com/watch?v=UAcCIsrAq70
• Hwang, C. (2021). Google Cloud's Biggest Customer Is Apple?. Digital Today. http://www.digitaltoday.co.kr/news/articleView.html?idxno=407723
• John, G., & David, R. (2011). Extracting value from chaos. IDC iView, 1.
• President's Council of Advisors on Science and Technology. (2011). Report to the President: Every Federal Agency Needs a 'Big Data' Strategy. https://www.prnewswire.com/news-releases/report-to-the-president-every-federal-agency-needs-a-big-data-strategy-118433704.html
• Executive Office of the President. (2012). Bigdata across the federal government. U.S. Government Printing Office.
• Ham, Y., & Chae, S. (2012). Big Data Changes Management. Samsung Economic Research Institute.
• Starbucks Korea. (2021). The coffee trend for 2021 predicted by Starbucks big data is “H.O.P.E.”. Starbucks Korea. https://www.starbucks.co.kr/bbs/getBodoView.do?seq=4040
• Jeong, B. (2022). Yun Seok-yeol elected the 20th president…Ultra-thin competition as predicted by big data. Ai Times. http://www.aitimes.com/news/articleView.html?idxno=143368
• Kim, S., Cho, H., and Kang, J. (2016). (The)Status of Using Text Mining in Academic Research and Analysis Methods. Korea Institute of Enterprise Architecture, 13(2), 317-329.
• Han, S., & Lee, M. (2012). A Big Data Model for Social Information Recommendation Techniques. Communications of the Korean Institute of Information Scientists and Engineers, 39(6), 380.
• Yoon, S., Namgung, H., Yang, S., and Kim, H. (2012). Big data-based large-capacity semantic web search technology trend. The Journal of Korean Institute of Communications and Information Sciences, 29(11), 24-29.
• Suh, Y. (2021). An Analysis of Security Issues Caused by COVID-19: Focusing on News Data. Chung-Ang University.
• Jeong, J. (2012). Three factors for successful big data utilization: resources, technology, and human resources. Korea Information Society Agency.
• Matthew A, R. (2013). Mining the social web. O'Reilly.
• Lee, B., Lim, J., and Yoo, J. (2013). Utilization of Social Media Analysis using Big Data. The Journal of the Korea Contents Association, 13(2). 211.
• Jeon, C., Seo, I. (2013). Analyzing the Bigdata for Practical Using into Technology Marketing : Focusing on the Potential Buyer Extraction. Journal of Marketing Studies, 21(2), 81-203.
• Lee, S. (2013). Network Analysis Methodology. Seoul: NONHYUNG, 11.
• Jeong, C. (2020). A Comparative Study on Attribute Recognition and Word of Mouth Intention of SNS Advertising - Focused on Facebook, Instagram, KaKaoStory and Twitter. The Journal of the Convergence on Culture Technology (JCCT), 6(2), 421.