• Human Ecology Research
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• v.60 no.1
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• pp.1-20
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• 2022

This paper presents a social network analysis of changes in Home Economics education content loaded on YouTube before and after the outbreak of COVID-19. From January 1, 2008 to June 30, 2021, a basic analysis was conducted of 761 Home Economics education videos loaded on YouTube, using NetMiner 4.3 to analyze important keywords and the centrality of video titles and full texts. Before COVID-19, there were 164 Home Economics education videos posted on YouTube, increasing significantly to 597 following the emergence of the pandemic. In both periods, there was more middle school content than high school content. The content in the child-family field was the most, and the main keywords were youth and family. Before COVID-19, a performance evaluation indicated that the proportion of student content was high, whereas after the outbreak of the disease, teacher content increased significantly due to the effect of distance learning. However, compared with video use, the self-expression and participation of users were lower in both periods. The centrality analysis indicated that in the title, 'family' exhibited a high degree of both centrality and eigenvector centrality over the entire period. Degree centrality of the video title was found to be high in the order of class, online, family, management, etc. after the outbreak of COVID-19, and the connection of keywords was strong overall. Eigenvector centrality indicated that career, search, life, and design were influential keywords before COVID-19, while class, youth, online, and development were influential keywords after COVID-19.

• Information Systems Review
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• v.18 no.1
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• pp.141-155
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• 2016

This study identified terminologies related to the proximity and frequency of disaster by social network analysis (SNA) and text mining, and then expressed the outcome into a mind map. The termdocument matrix of text mining was utilized for the terminology proximity analysis, and the SNA closeness centrality was calculated to organically express the relationship of the terminologies through a mind map. By analyzing terminology proximity and selecting disaster response-related terminologies, this study identified the closest field among all the disaster response fields to disaster response and the core terms in each disaster response field. This disaster response terminology analysis could be utilized in future core term-based terminology standardization, disaster-related knowledge accumulation and research, and application of various response scenario compositions, among others.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.169-186
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• 2020

This study selected 127 powerful Korean film producers, directors, and actors whose stable audience drawing power has been proven over the past seven years from 2013 to 2019, and viewed their network through social network analysis(SNA) to explain their power structure. It also explained the changes compared to the results of previous studies conducted on box office hits from 1998 to 2012. The producers who showed the highest audience drawing power over the past seven years were KANG Hae-jung, JANG Won-seok, LEE Eugene, HAN Jae-duk. BONG Joon-ho, KIM Yong-hwa, and RYOO Seung-wan as directors and SONG Kang-ho, HA Jung-woo, and HWANG Jung-min as actors were confirmed to exhibit the most stable audience drawing power. Meanwhile, the network formed by the 127 leading producers, filmmakers, and actors was analyzed based on closeness/ degree/eigenvector/betwenness centrality, and the result discovered a strong network involving JANG Won-seok, HAN Jae-duk, CHO Jin-woong, Don LEE, and HWANG Jung-min. This study is meaningful in that it included producers, the position which has never been discussed in previous local studies to analyze the network influencing star casting, and selected accurate box office hits by checking whether the concerned films actually reached break-even point rather than simply relying on the number of audiences or total revenue they garnered. Nonetheless, it left a hole to be filled in that it did not include the role of the management companies in the network. Therefore, a relevant follow-up discussion would be needed.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.49-65
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• 2015

As South Korean large business groups, also known as Chaebol, have broadened their influence in the domestic economy, it is important to analyze the influence of internal transactions among Chaebol affiliates on their performance. In this paper, relationship between internal transactions and efficiency of Chaebol affiliates has been analyzed. Top five Chaebol groups in South Korea are selected; they include Samsung, Hyundai Motors, LG, SK, and Lotte group. Based on internal transactions among affiliates, social networks are constructed for each Chaebol group to analyze centrality, network structures and cliques. Data Envelopment Analysis (DEA) was conducted to examine the efficiency of the Chaebol affiliates. Then, correlations between the degree centrality and the efficiency of Chaebol affiliates were analyzed, and the network structures of Chaebol groups are presented. The result shows that positive correlations between degree centrality and efficiency are observed among four Chaebol Groups. This paper shows that the Social Network Analysis (SNA) techniques can be used in the empirical research for the analysis of internal transactions of Chaebol groups.

• Human Ecology Research
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• v.56 no.6
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• pp.603-617
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• 2018

This study analyzed research trends in the field of Practical Arts (Technology & Home Economics) education. From 958 articles published between 2010 and 2018 in the Journal of Korean Practical Arts Education (JKPAE), Journal of Korean Home Economics Education Association (JHEEA), and Korean Journal of Technology Education Association (KJTEA), 958 keywords were extracted and analyzed using NetMiner 4. When the general network structure was analyzed, keywords such as practical arts education, curriculum, textbook, home economics education, and students were high in the degree centrality and closeness centrality, and textbook, practical arts education, curriculum, student, home economics education, and invention were high in the node betweenness centrality. The cluster analysis showed that a four-cluster solution was most appropriate: cluster 1, technology and experiential learning activities; cluster 2, curriculum studies and practical problem; cluster 3, relationships; and cluster 4, creativity and character education. The three journals showed differences in the knowledge network structure: The topics of JKPAE and JKHEEA focused on general content knowledge and curriculum, while the topics of KJTEA were spread across invention and creativity education, and curriculum studies.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.6
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• pp.1058-1073
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• 2022

Big data analytics and social media have shifted the way fashion trends are dictated. Fashion as a medium for expressing gender has created new concepts of masculinity in popular culture, where men are increasingly depicted in a softer style. In this study, we analyzed 2,879 menswear collections over a 10-year period from Vogue US to uncover key menswear trends. Using Semantic Network Analysis (SNA) on Orange3, we were able to quantitatively analyze how contemporary menswear designers interpreted diversified trends of masculinity on the runway. Frequency and degree centrality were measured to weigh the significance of trend keywords. "Jacket (f = 3056; DC = 0.80), shirt (f = 1912; DC = 0.60) and pant (f = 1618; DC = 0.53)" were among the most prominent keywords. Our results showed that soft masculine keywords, e.g., "lace, floral, and pink" also appeared, but with the majority scoring DC = < 0.10. The findings provide an insight into key menswear trends through frequency, degree centrality measurements, time-series analysis, egocentric, and visual semantic networks. This also demonstrates the feasibility of using text analytics to visualize design trends, concepts, and patterns for application as an ideation tool for academic researchers, designers, and fashion retailers.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.201-210
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• 2020

Navy submarines are developed into complex weapons systems with various equipment, which directly leads to difficulties in submarine maintenance. In addition, the method of establishing a maintenance plan for submarines is limited in efficient maintenance because it relies on statistical access to the number of people, number of target ships, and consumption time. For efficient maintenance, it is necessary to derive and maintain major maintenance factors based on an understanding of the target. In this paper, the maintenance loads rate is defined as a key maintenance factor. the submarine maintenance data is analyzed using the SNA scheme to identify phenomena by focusing on the relationship between the analysis targets. Through this, maintenance loads characteristics that have not been previously revealed in quantitative analysis are derived to identify areas that the maintenance manager should focus on.

• Journal of Korean Library and Information Science Society
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• v.44 no.4
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• pp.443-463
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• 2013

Korea Institute of Science and Technology Information(KISTI) provides domestic researchers with science and technology information through NDSL Information Document Service(NIDS) network to improve research productivity in Korea. University libraries and information centers of research institutes are playing a major role in the NIDS collaboration network. In this study, we examined the relationship among the participating organizations for document delivery service using the social network analysis(SNA) method. Centrality of each organization in the NIDS network was analyzed with the indexes such as degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The research results show that KISTI, KAIST, POSTECH, and FRIC are located at the center of the NIDS network. Based on the research results, this paper suggests several directions for improvement of document delivery service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3571-3587
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• 2021

For accurately analysing and forecasting the social networks of China's political, economic and social power elites, it is necessary to develop a database that collates their information. The development of such a database involves three stages: data definition, data collection and data quality maintenance. The present study recommends distinctive solutions in overcoming the challenges that occur in existing comparable databases. We used organizational and event factors to identify the Chinese power elites to be included in the database, and used their memberships, social relations and interactions in combination with flows data collection methodologies to determine the associations between them. The system can be used to determine the optimal relationship path (i.e., the shortest path) to reach a target elite and to identify of the most important power elite in a social network (e.g., degree, closeness and eigenvector centrality) or a community (e.g., a clique or a cluster). We have used three social network analysis tools (i.e., R, UCINET and NetMiner) in order to find the important nodes in the network. We compared the results of centrality rankings of each tool. We found that all three tools are providing slightly different results of centrality. This is because different tools use different algorithms and even within the same tool there are various libraries which provide the same functionality (i.e., ggraph, igraph and sna in R that provide the different function to calculate centrality). As there are chances that the results may not be the same (i.e. centrality rankings indicating the most important nodes can be varied), we recommend a comparison test using different tools to get accurate results.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.169-180
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• 2021

This study analyzed research trends on smart logistics in Korea by conducting social network analysis (SNA) using R. The purpose of this study is to enhance the understanding of the smart logistics field along with the smart logistics status and policy review, and to suggest implications and future research tasks. The analysis period and subjects were 128 academic journal papers on smart logistics-related topics over the past 10 years (2011-2020), and the results were divided into the first half (2011-2015) and the second half (2016-2020). The analysis of the study was carried out step-by-step through frequency analysis, network centrality analysis, and visualization. As a result of the study, the quantitative increase and diversification of the research field were confirmed. It was also confirmed that the concentration of research on core areas increased by increasing the proportion of duplicate keywords. In addition, keywords with high betweenness centrality and degree centrality score such as Logistics, Authentication, Smart, Service, RFID, Technology, and Revolution were presented. Lastly, this study reviewed the structure with a focus on betweenness centrality after visualizing the network with main keywords. As a result, early research was focused on the field of logistics, and after 2016, it was confirmed that the center of research was diversifying and expanding with the development of the 4^th industrial revolution and major technologies.