유튜브를 활용한 코로나 시대의 운동 이슈 의미연결망 분석 (Semantic Network Analysis on the Exercise Issues in the Covid-19 Using YouTube)

서지정보

· 발행기관 : 한국사회체육학회
· 수록지 정보 : 한국사회체육학회지 / 85호 / 187 ~ 198페이지
· 저자명 : 김경식

초록

Purpose: This study aims to analyze key keywords related to exercise and to identify the types, weights and trends of topics. The research question is as follows. First, hwat is the pattern of the high-ranking movemen t keywords? Second, how are sports topics appearing? Method: In this study, 495 videos were collected from YouTube in order to investigate the pattern of movement keywords in the Corona era using YouTube. In this study, video data were collected through NetMiner’s SNS data collection, and frequency analysis, centrality analysis, and topic modeling analysis were conducted.
Result: First, the frequency of exercise keywords in the Corona era using YouTube appeared in the order of video, gratitude, people, body, degree, thinking, start, mucsle, time, strength, and well-being. From a rel-a tional point of view, the main words of exercise were high in centralities such as image, degree, body, gratitude, person, strength, muscle, time, day, diet, routine, health, movement, and home training. Second, exercise topics in the Corona era using YouTube were weighted in order fo exercise & nutrition, exercise method , exercise & diet, exercise & social media, exercise & injury, hmoe training, exercise & health, and exercise & effect. Looking at the trend of the topics, the topics of exercise method, exercise & diet, and exercise & nutrition were on an increasing trend overall, and the topics of exercise and health, exercise and effectiveness had been discontinued. And although exercise & injury, and exercis e& social media topics were disconnected , they were on the rise, and home training continues to increase and decrease.
Conclusion: Semantic network analysis through text mining can be said to be an important research method in understanding the interactions related to movement in social media.

영어초록

Purpose: This study aims to analyze key keywords related to exercise and to identify the types, weights and trends of topics. The research question is as follows. First, hwat is the pattern of the high-ranking movemen t keywords? Second, how are sports topics appearing? Method: In this study, 495 videos were collected from YouTube in order to investigate the pattern of movement keywords in the Corona era using YouTube. In this study, video data were collected through NetMiner’s SNS data collection, and frequency analysis, centrality analysis, and topic modeling analysis were conducted.
Result: First, the frequency of exercise keywords in the Corona era using YouTube appeared in the order of video, gratitude, people, body, degree, thinking, start, mucsle, time, strength, and well-being. From a rel-a tional point of view, the main words of exercise were high in centralities such as image, degree, body, gratitude, person, strength, muscle, time, day, diet, routine, health, movement, and home training. Second, exercise topics in the Corona era using YouTube were weighted in order fo exercise & nutrition, exercise method , exercise & diet, exercise & social media, exercise & injury, hmoe training, exercise & health, and exercise & effect. Looking at the trend of the topics, the topics of exercise method, exercise & diet, and exercise & nutrition were on an increasing trend overall, and the topics of exercise and health, exercise and effectiveness had been discontinued. And although exercise & injury, and exercis e& social media topics were disconnected , they were on the rise, and home training continues to increase and decrease.
Conclusion: Semantic network analysis through text mining can be said to be an important research method in understanding the interactions related to movement in social media.

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