• 한국노년학
• /
• v.37 no.2
• /
• pp.329-347
• /
• 2017

The purpose of this study is to see if we can predict the health of seniors of community by analyzing the connection between social network degree and mental and physical health of old people who live in the areas of Gangwha Island. The subjects of the study were men and women aged 65 or over, a total of 643 that resided in Ganghwa A-county. The survey was conducted on Korean Social Life, Health and Aging Project from the year 2011 to 2012. Regression analysis was carried out using the data. The analysis results were as follows. First, it showed the relationships between income, gender, age out of demographic variables used as control variable and old persons'physical health. The research results showed that physical health was better in case of the higher incomes, men, and lower age. Second, out of demographic variables, educational background, income, age was shown to correlate with mental health. The research results showed that mental health was better in case of the higher incomes, higher educational background, and lower age. Third, in social network including direction, both out-degree and in-degree were shown to predict old people's physical and mental health. The results of this study suggest that not only out-degree but also in-degree should be considered in predicting the health of elderly persons by a person's human relationship. Also, two indicators of degree are meaningful in the dimension of health promotion and welfare of the old in that they can be used for finding isolated individuals that can be physically and mentally vulnerable.

• Journal of Korea Technology Innovation Society
• /
• v.17 no.2
• /
• pp.334-354
• /
• 2014

This study aims to analyze the research trend of Korean medicine based on social network analysis. To do this, a dataset has been collected from KCI (Korea Citation Index) database. According to the results, we have identify the longitudinal trend of the number of papers, journals, organizations and key words in this field. Moreover, based on the nodes' centrality of co-author network, we have found a core journal (i.e. Korean Journal of Oriental Physiology and Pathology), a hub institution (i.e. Kyunghee university) and two main key words (i.e. anti-oxidation and acupuncture) in the research network. In conclusion, integrating field experts' tacit knowledge in Korean medicine studies with the results of the explicit social network analysis on the research trend, we put forward further policy implications with regard to R&D strategies in this field.

• Journal of the Korean Geographical Society
• /
• v.49 no.1
• /
• pp.57-76
• /
• 2014

Conventional treatments of war diffusion focus extensively on dyadic relationships, whose impact is thought to be immutable over the course of the conf lict. This study indicates that such conceptions are at best incomplete, and more likely misleading to explain the spatial diffusion of wars. Using social network analysis, we examine war joining behavior during World War I. By employing social network analysis, we attempted to overcome the dichotomous understanding of geography as space and network in the discipline of conflict studies. Empirically, networked structural elements of state relationships (e.g., rivalry, alliances) have explanatory and predictive value that must be included alongside dyadic considerations in analyzing war joining behavior. In addition, our analysis demonstrates that the diffusion of conflict involves different driving forces over time.

• The Journal of the Korea Contents Association
• /
• v.17 no.7
• /
• pp.443-454
• /
• 2017

A positive destination image has an impact on the tourist arrivals and economic growth of the tourist destination. Recently, the content generated by sharing tourist experiences and destination information on the internet has been increasing. The online content has the potential to become a major tourist decision source and provide more in-depth materials and richer content to extract destination image, insight and tourist's perceptions of the destination. This study was designed to explore the destination image of Daegu online and draw lessons for successful image management in an era of big data. Text mining approach and social network analysis were conducted to extract destination image determining elements and assess the influence of the elements. The result showed that destination image elements related to tourist infra-structures and culture, history and art affected the overall destination image of Daegu. Destination marketers should make an effort to grasp these precise destination image and seek ways to boost competitiveness as a tourist destination.

• Korean Journal of Social Welfare Studies
• /
• v.45 no.2
• /
• pp.147-172
• /
• 2014

The purpose of this study is to analyze the cause-and-effect relationships among positive verbal expressions from family members, social network, and disability identity as the determinant factors of intention to work. To achieve the goal, 453 persons with disabilities and with experiences of using facilities for the disabled were studied, who were recruited for the national survey of domestic violence in 2010. For the research subjects, the cause-and-effect relationships among the major factors were examined by using a structural equation model. According to the results, disability identity and intent to work tended to be higher as they had experienced more of positive verbal expressions from their families. Moreover, a more participation in social activities increased disability identity, but not intent to work. Lastly, disability identity was found to be a determinant of intent to work. Based on the results, we provide suggestions to achieve social inclusion of people with disabilities by raising the intent to work.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.3
• /
• pp.114-126
• /
• 2016

Domestic risk factors that are thought to be correlated with highly pathogenic avian influenza (HPAI) outbreak are migratory birds and moving objects such as poultry farm vehicles. In particular, the commercial vehicles that routinely circulate the local and/or remote poultry farms produce are thought to be major HPAI risk factors in South Korea. In this study, the driving histories of the vehicles belonging to poultry farms and/or commercial companies registered in the Korea Animal Integrated System (KAHIS) were analyzed using statistical and social networking tools in a Geographic Information System (GIS) in order to understand the pattern of the HPAI (H5N8) outbreak that occurred in 2014 in South Korea. Based on the 2014 HPAI outbreak patterns, HPAI-infected poultry farms were categorized according to geological features. The HPAI-infected poultry farms were categorized as 'regional-accumulation', 'regional-distribution', 'metropolitan-accumulation', 'metropolitan-distribution' and 'national-distribution' in endemic or non-endemic regions. We were able to categorize most HPAI-infected poultry farms into the five proposed categories, but further studies are required to categorize all such farms. Based on this categorization system, we propose efficient but economical prevention boundaries in South Korea. We strongly believe that our research could hugely impact government decisions to estimate the prevention area.

• Journal of Digital Convergence
• /
• v.12 no.5
• /
• pp.57-68
• /
• 2014

This article analyses frame transition of personal information leakage in Korea from 1984 to 2014. In order to investigate the transition, we have collected newspaper article's titles. This study adopts classification, text network analysis(by co-occurrence symmetric matrix), and clustering techniques as part of social network analysis. Moreover, we apply definition of centrality in network in order to reveal the main frame formed in each of four periods. As a result, accessibility of personal information is extended from public sector to private sector. The boundary of personal information leakage is expanded to overseas. Therefore it is urgent to institutionalize the protection of personal information from a global perspective.

• The Journal of the Korea Contents Association
• /
• v.13 no.7
• /
• pp.16-23
• /
• 2013

This paper analyzes the artistic practical use of social network visualization through the aesthetic information interpretation. The first social network visualization has emerged as 'Sociogram' in the form of social network analysis(SNA). Since social network has complex, the analyzing technology of social network has emerged. Early social network visualization has a practical purpose to measure the social structure, but current social network visualization divided into various forms from artistic expressions through information. This paper divide into two categories based on the artistic application of social network visualization. First, this research focuses on the static graph based on analog. Second, this research analyze the category of interacted visualization to generate a real-time digital image. This paper presents the fusion of paradigm between engineering and art through this way.

• Journal of the Society of Disaster Information
• /
• v.20 no.2
• /
• pp.462-469
• /
• 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 Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.107-120
• /
• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.