• Korea Trade Review
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• v.47 no.4
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• pp.137-159
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• 2022

This study aims to analyze international trade papers published in Korea during the past 2002-2022 years. Through this study, it is possible to understand the main subject and direction of research in Korea's international trade field. As the research mythologies, this study uses the big data analysis such as the text mining and Social Network Analysis such as frequency analysis, several centrality analysis, and topic analysis. After analyzing the empirical results, the frequency of key word is very high in trade, export, tariff, market, industry, and the performance of firm. However, there has been a tendency to include logistics, e-business, value and chain, and innovation over the time. The degree and closeness centrality analyses also show that the higher frequency key words also have been higher in the degree and closeness centrality. In contrast, the order of eigenvector centrality seems to be different from those of the degree and closeness centrality. The ego network shows the density of business, sale, exchange, and integration appears to be high in order unlike the frequency analysis. The topic analysis shows that the export, trade, tariff, logstics, innovation, industry, value, and chain seem to have high the probabilities of included in several topics.

• 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.

• Korea Trade Review
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• v.47 no.2
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• pp.121-132
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• 2022

This study aims to find out the ESG management keywords in the logistics industry through social network analysis using news article and sustainable management reports. In recent years, global climate change and Covid-19 have spurred companies to step up their new management system called ESG management. ESG is a combination of Environment, Social, and Governance. In the past, companies' financial performance was the most important, but in the current investment market, the movement to reflect ESG management factors in investment decisions is strengthening. This study aims to find out degree centrality, betweenness centrality, and closeness centrality through social network analysis after collecting related keywords to derive ESG management issues of logistics companies. This study collected 2,359 news articles searched under the keywords "ESG", "Logistics". In addition, data on ESG activities were also used for analysis by referring to the sustainable management reports of logistics companies. As a result of the analysis of degree centrality, it was found that ESG management of logistics companies is in progress, focusing on small enterprises and eco-friendly keywords, and is concentrated on social responsibility and eco-friendly activities. In the betweenness centrality analysis, logistics companies such as HMM and CJ Logistics were derived in a high ranking. In the closeness centrality analysis, eco-friendly keywords topped the list, while the number of keywords related to governance was relatively small, suggesting that logistics companies need to improve their governance structure.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.462-469
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• 2024

Purpose: This study aims to analyze data on disaster safety education for migrant youth and to examine the corresponding social perceptions. Method: Data on disaster safety education for migrant youth were collected and analyzed using Textom and Ucinet. The data used in the study were searched on portal websites from 2016 to 2023 using the keywords 'migrant youth+ disaster + safety education'. Result: The analysis results showed that 'education (306)' had the highest frequency, followed by 'safety (287)', 'school (97)', 'society (85)', and 'support (77)'. The keyword with the high degree of centrality, closeness centrality, and betweenness centrality were 'education', 'safety' and 'society'. 'Family' ranked higher in betweenness centrality than the rankings of frequency analysis, degree centrality and closeness centrality, indicating that 'family' plays a significant role as a mediator in the network of disaster safety education for migrant youth. Conclusion: By examining social awareness about disaster safety education for migrant youth, the findings will be used to develop policies and strategies for disaster safety education that consider the unique vulnerabilities of migrant youth in disaster situations.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.47-56
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• 2020

The purpose of this study was to identify the research trends and characteristics of existing research related to the AEO system. The methodology of the study was to utilize the Degree Centrality, Closeness Centrality and Betweenness Centrality presented by the Social Network Analysis (SNA). Keyword network analysis results showed that "MRA", "Logistics Security" were derived from the Degree Centrality results, "MRA", "Logistics Security" from the Closeness Centrality results, and, as a result of the Betweenness Centrality, "AEO Utilization Benefits" and "reliability" were derived from the top keyword results. The analysis of differences in centrality by period also confirmed that trends in research have changed based on specific time points. This study has implications for the study in that it presented worldwide research trends through keyword network analysis of the AEO system.

• Journal of Distribution Research
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• v.16 no.2
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• pp.95-118
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• 2011

The purpose of this study is as follows. First, we investigate empirically the effects of social network properties such as social network density and centrality of a franchisee on its information sharing with various subjects such as the franchisor and other franchisees in the franchise system. Second, we examine exploratively if tie strength between a franchisee and its franchisor plays a moderating role on the relationship between social network properties and information sharing. The study model was established as shown in

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of the Korean Society of Costume
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• v.65 no.6
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• pp.25-35
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• 2015

The purpose of this study is to investigate the characteristics of members and communities that have significant influence in the online fashion community through their word-of-mouth activities. In order to identify the influence and the diffusion of word-of-mouth in fashion community, the study selected one online fashion community. Then, the study sorted the online posts and comments made on fashion information and put them into the matrix form to perform social network analysis. The result of the analysis is as follows: First, the fashion community network used in the study has many active members that relay information very quickly. Average time for information diffusion is very short, taking only one or two days in most cases. Second, the influence of word-of-mouth is led by key information produced from only a few members. The number of influential members account for less than 20% of the total number of community members, which indicate high level of degree centrality. The diffusion of word-of-mouth is led by even fewer members, which represent high level of betweenness centrality, compared to the case of degree centrality. Third, component characteristic shares similar information with about 70% of all members being linked to maximize information influence and diffusion. Fourth, a node with high degree centrality and betweenness centrality shares similar interests, presenting strain effect to particular information. Specially, members with high betweenness centrality show similar interests with members of high degree centrality. The members with high betweenness centrality also help expansion of related information by actively commenting on posts. The result of this research emphasizes the necessity of creation and management of network to efficiently convey fashion information by identifying key members with high level of information influence and diffusion to enhance the outcome of online word-of-mouth.

• The Journal of Information Systems
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• v.32 no.2
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• pp.177-190
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• 2023

Purpose Recently, as a new business marketing tool, short form content focused on fun and interest has been shared as hashtags. By extracting positive and negative keywords from media audiences through comment analysis of social media news, various stakeholders aim to quickly and easily grasp users' opinions on major news. Design/methodology/approach YouTube videos were searched using the YouTube Data API and the results were collected. Video comments were crawled and implemented as HTML elements, and the collection results were checked on the web page. The collected data consisted of video thumbnails, titles, contents, and comments. Comments were word tokenized with the R program, comparing positive and negative dictionaries, and then quantifying polarity. In addition, social network analysis was conducted using divided positive and negative comments, and the results of centrality analysis and visualization were confirmed. Findings Social media users' opinions on issue news were confirmed by analyzing and visualizing the centrality of keywords through social network analysis by dividing comments into positive and negative. As a result of the analysis, it was found that negative objective reviews had the highest effect on information usefulness. In this way, previous studies have been reaffirmed that online negative information has a strong effect on personal decision-making. Corporate marketers will analyze user comments on social network services (SNS) to detect negative opinions about products or corporate images, which will serve as an opportunity to satisfy customers' needs.

• Korea Trade Review
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• v.46 no.1
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• pp.113-134
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• 2021

This study analyzed the centrality of the GVCs network and the value-added-based production structure of the electrical and electronic industries using ADB-MIRO and social network analysis methods. According to the analysis, the centrality and power of the GVSc intermediate goods network were differentiated into China, the United States, and the EU due to the advancement of industrial structure in Asia. In the 2000 network, the United States and Japan had a very strong influence in all aspects, including connectivity and strength. However, in 2017, China's power index rose to number one among 62 countries in the network. Furthermore, this study presented strategic implications of the Korean electrical and electronic industries to respond to the reorganization of GVSs based on the analysis results.