Big Data Analysis on the Educational Potential of YouTube Sports Content

Objectives This study aims to identify the educational potential of YouTube sports content through big data analysis of YouTube sports content. Methods ‘YouTube sports’ was selected as a keyword and data was collected using the big data program TextoM. The collected keywords were analyzed by selecting the top 50 words for intensive analysis. The analysis channels included Naver and Daum news, blogs, and Cafe Knowledge In. Results First, the results of the analysis of major keywords related to the game showed that players, official, Hanwha Life Insurance, game, public, season, and fans were high, indicating high interest in game information. Second, the results of the analysis of major keywords related to sports skills education showed that keywords such as YouTube, school, indoor, club, lesson, class, physical education, basketball, children, program, volleyball, ski, and performance were high, indicating high interest in sports skills education. Third, the results of the analysis of major keywords related to sports academies showed that keywords such as sports, monster, academy, soccer, star, and team were high, indicating high interest in sports academies. Fourth, the results of the analysis of major keywords related to online education show that keywords such as Korea, Daum, broadcast, provision, content, game, channel, Stars, news, analysis, and economy are high, indicating that interest in online education is high. Conclusions Therefore, consumption of YouTube sports content is now valuable as an important learning tool that can acquire knowledge about various sports beyond simple interest and entertainment.