scholarly journals Big Data Analysis of the Key Attributes Related to Stress and Mental Health in Korean Taekwondo Student Athletes

2022 ◽  
Vol 14 (1) ◽  
pp. 477
Author(s):  
Sung-Un Park ◽  
Jung-Woo Jeon ◽  
Hyunkyun Ahn ◽  
Yoon-Kwon Yang ◽  
Wi-Young So
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Correlation Analysis ◽  
Student Athletes ◽  
Big Data Analysis ◽  
Degree Centrality

In the present study, we used big data analysis to examine the key attributes related to stress and mental health among Korean Taekwondo student-athletes. Keywords included “Taekwondo + Student athlete + Stress + Mental health”. Naver and Google databases were searched to identify research published between 1 January 2010 and 31 December 2019. Text-mining analysis was performed on unstructured texts using TEXTOM 4.5, with social network analysis performed using UCINET 6. In total, 3149 large databases (1.346 MB) were analyzed. Two types of text-mining analyses were performed, namely, frequency analysis and term frequency-inverse document frequency analysis. For the social network analysis, the degree centrality and convergence of iterated correlation analysis were used to deduce the node-linking degree in the network and to identify clusters. The top 10 most frequently used terms were “stress”, “Taekwondo”, “health”, “player”, “student”, “mental”, “exercise”, “mental health”, “relieve”, and “child.” The top 10 most frequently occurring results of the TF-IDF analysis were “Taekwondo”, “health”, “player”, “exercise”, “student”, “mental”, “stress”, “mental health”, “child” and “relieve”. The degree centrality analysis yielded similar results regarding the top 10 terms. The convergence of iterated correlation analysis identified six clusters: student, start of dream, diet, physical and mental, sports activity, and adult Taekwondo center. Our results emphasize the importance of designing interventions that attenuate stress and improve mental health among Korean Taekwondo student-athletes.

scholarly journals A study on the influence of Korean Middle School students' relationship through Science class applying STAD cooperative learning

2018 ◽  
Vol 8 (4) ◽  
pp. 291 ◽  
Author(s):  
Dongryeul Kim
Keyword(s):  
Middle School ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Middle School Students ◽  
Collaborative Learning ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Science Class ◽  
School Students

  In order to find out the influence of Korean Middle School Students' relationship by science class applying STAD collaborative learning, this study conducted a social network analysis and sought to analyze the communication networks within the group and identified the change process of the type. The subject of this study was 30 students of the second grade at the girls' middle school located in Korea's Metropolitan City. For five weeks, science class applying STAD Collaborative Learning was implemented in the ‘reproduction and generation’ chapter. First, the class social network analysis showed that all the prices of density, degree centrality, closeness centrality, and betweenness centrality have risen after science class applying STAD Collaborative Learning. Also, the classroom's relationship index has improved. In other words, STAD Collaborative Learning encouraged interaction among students. Second, in order to research popularity, students' centrality analysis through the class social network analysis showed that top-ranked students' values of density, degree centrality, closeness centrality, and betweenness centrality appeared commonly high after science class applying STAD Collaborative Learning. Third, the analysis of the communication network change within six groups showed that all channel type appeared most often and circle type also appeared anew after science class applying STAD Collaborative Learning. In other words, it was possible to exchange information freely and communicate with all members of the group through STAD Collaborative Learning.

scholarly journals PEMETAAN PERILAKU BERISIKO GAY BERBASIS SNA SEBAGAI EARLY WARNING SYSTEM KASUS HIV/AIDS DI KOTA KENDARI

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Jumartin Gerung
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Betweeness Centrality ◽  
Hiv Aids

AbstrakPada kasus HIV dalam skala nasional, menunjukkan bahwa kelompok heteroseks juga termasuk sebagai kelompokutama yang paling berisiko menderita HIV/AIDS. Peningkatan ini mencolok terijadi sejak 2015 angkanya masih di 4.241 kasus, dan meningkat hingga lebih dari dua kali lipat pada 2016 yang mencapai 13.063 kasus. Data pemetaaninteraksi di sosial media khususnya wilayah Kendari terdapat sekitar 800 akun yang memberi interaksi perihal Gay.Hal ini diindikasikan akan mempengaruhi prevalensi kejadian HIV/AIDS di Kota Kendari. Penelitian ini bertujuan untukmemetakan interaksi perilaku berisiko Gay sebagai early warning system kasus HIV/AIDS. Social Network Analysismerupakan studi yang mempelajari tentang hubungan manusia dengan memanfaatkan teori graf. Penerapan SocialNetworkAnalysis dalam suatu aplikasi mampu menggambarkan relasi atau hubungan antar individu denganmelakukan visualisasi terkait centrality (titik pusat), between centrality (jalur pendek), juga closeness centrality yaknirata-rata jalur terpendek dari interaksi akun di laman FB. Untuk platform Facebook berdasarkan pada hasilpenghitungan diketahui bahwa akun yang berpengaruh terhadap interaksi jejaring sosial adalah akun Gay Kendariyang unggul pada nilai degree centrality,betweeness centrality, dan Closeness centrality. Akun Gay Kendari palingberpengaruh dalam interaksi jaringan sosial Facebook. Melalui social network analysis, penelitian ini memberikangambaran relasi perilaku berisiko LSL/Gay sebagai early warning system kasus HIV/AIDS di kota kendariKata kunci: analisis jaringan sosiai, gay, sistem peringatan dini, HIV/AIDS 

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