• The Research Journal of the Costume Culture
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• v.27 no.3
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• pp.193-205
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• 2019

A trend analysis of research articles in a field of knowledge is significant because it can help in finding out the structural characteristics of the field and the future direction of research through observing change in a time series. We identified the structural characteristics and trends in text data (keywords) gathered from research articles which in itself is an important task in various research areas. The titles and keywords were crawled from research articles published from 2016 to 2018 in the Research Journal of the Costume Culture (RJCC), one of the representative Korean journal in the field of clothing and textile. After we extracted data comprising English titles and keywords from 195 published articles, we transformed it into a 1-mode matrix. We used measures from network analysis (i.e., link, strength, and degree centrality) for evaluating meaningful patterns and trends in the research on clothing and textile. NodeXL was used for visualizing the semantic network. This study observed change in the clothing and textile research trend. In addition to covering the core areas of the field, the subjects of research have been diversifying with every passing year and have evolved onto a developmental direction. The most studied area in articles published by the RJCC was fashion retailing/consumer psychology while aesthetic/historic and fashion industry/policy studies were covered to a more limited extent. We observed that most of the studies reflecting the identity of RJCC share subject keywords to a significant extent.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.2
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• pp.175-185
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Gestational diabetes mellitus (GDM) and Breastfeeding' field of research for better understanding research trends in the past 20 years. Methods: This was a text-mining and topic modeling study composed of four steps: 1) collecting abstracts, 2) extracting and cleaning semantic morphemes, 3) building a co-occurrence matrix, and 4) analyzing network features and clustering topic groups. Results: A total of 635 papers published between 2001 and 2020 were found in databases (Web of Science, CINAHL, RISS, DBPIA, RISS, KISS). Among them, 3,639 words extracted from 366 articles selected according to the conditions were analyzed by text network analysis and topic modeling. The most important keywords were 'exposure', 'fetus', 'hypoglycemia', 'prevention' and 'program'. Six topic groups were identified through topic modeling. The main topics of the study were 'cardiovascular disease' and 'obesity'. Through the topic modeling analysis, six themes were derived: 'cardiovascular disease', 'obesity', 'complication prevention strategy', 'support of breastfeeding', 'educational program' and 'management of GDM'. Conclusion: This study showed that over the past 20 years many studies have been conducted on complications such as cardiovascular diseases and obesity related to gestational diabetes and breastfeeding. In order to prevent complications of gestational diabetes and promote breastfeeding, various nursing interventions, including gestational diabetes management and educational programs for GDM pregnancies, should be developed in nursing fields.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.648-654
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• 2016

This paper investigates differences of tourism destination choices for sightseeing in Seoul between first-time visitors and repeat visitors. We constructed social network using secondary data from '2015 International Visitor Survey' and analyzed its density and centrality. Study results find that: (1) first-time and repeat visitors' tourism destinations are concentrated in areas located north of the Han river. The proximity of destinations suggests the positive effects resulting from the movement network. (2) As the result of degree centrality, closeness centrality, betweenness centrality, the highest ranking tourism destinations for both visitor groups are identical, but indexes of centralities in repeat visitors' destinations increase, including Shinchon/ Hongik University, Gangnam station, and Garosu-gil. Therefore, the roles of these destinations are becoming established as tourism hubs and are popular among younger visitors as well as attract repeat visitors. Results of this study will be a useful reference in developing and managing new tourism products.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.193-203
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• 2024

In a situation where news usage through YouTube is rapidly increasing, this study investigated which attributes of issues news producers prominently report on based on the two-stage agenda setting theory to empirically investigate the influence of various news producers on YouTube. Through the research results, we confirmed that broadcasters have the influence to set the agenda and form public opinion on YouTube, and discovered the possibility of a two-stage agenda setting effect occurring in the YouTube environment. We criticized whether news producers abuse emotional words due to their partisanship when reporting political issues, and discussed that an emotional approach to political issues can have a negative impact on news users' perception of reality.

• Management & Information Systems Review
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• v.35 no.1
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• pp.253-271
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• 2016

This study is to investigate the impact of the social capital on organization trust, and the impact of the organization trust on recommendation intention, and turnover intention. And by this it is also to integrally analyze through what route social capital affects the recommendation intention and turnover intention. An actual analysis through covariance structural equation model was made targeting the members of small and medium sized manufacturing companies. The results of the actual analysis showed that the relational dimension in the social capital had an positive(+) and the most pervasive effect on the organization trust. Relational dimension refers to the formation relationships among members and has a significant value in the interaction in the relation between subordinates and superiors, between colleagues, and between departments. Secondly, the cognitive dimension in the social capital was revealed to have no significant effect on the organization trust and structural dimension was revealed to have a positive(+) effect on the organization trust. Structural dimension refers to the capital value which shows itself in the social network and relationship existing between the members and is formed through building the best network within an organization. Thirdly, organization trust was revealed to have a positive(+) impact on the recommendation intention and to have a negative(-) impact on the turnover intention. Finally, the summary, implications, limitations, and future research direction of this study were presented.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.561-568
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• 2006

WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.

• Journal of the Korean Regional Science Association
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• v.34 no.1
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• pp.31-47
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• 2018

This study examines the locational characteristics and change of business type in the residental areas that have commercial gentrification issues in Seoul, Korea, using the logistic regression model. The analysis results indicate that the gentrification occurrence areas are strongly associated with low-density and old residential areas. In addition, those areas are more likely to have great accessibilities to highway ramp, subway station, colleges, and other facilities that attract people. Regarding the characteristics of the road, gentrification occurrence areas are associated with longer road length, lower rate of road areas, higher local integration of road network, and higher rate of three-way intersections. This finding indicates that low-density and old residential areas with organic road networks have strong links with commercial gentrification. This study also finds that the business type has been substantially changed from 2006 to 2014 in the commercially gentrified residential areas. While the coffee shops and drinking places have been increased, but neighborhood-living facilities have been decreased. This study also shows that the business life-cycles of drinking places or Korean restaurant are getting short. Finally, this study discusses the commercial gentrification issues and policy implications in the residential districts in Seoul, Korea.

• 한국노년학
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• v.37 no.2
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• pp.329-347
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• 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.

• Korean Journal of School Psychology
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• v.18 no.1
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• pp.31-48
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• 2021

The purpose of this study was to determine whether the retention of university freshmen could be predicted using their peer relationships in a specific department. In this study, retention was defined as a student staying enrolled in their university for a certain period of time. Social relationships are formed through interaction between people, so both students' self-perceptions and others' perceptions of them must be accounted for, so we used a social network analysis that did so. We examined social networks visualizations that allowed for a rich interpretation of numerical information. Participants in this study were freshmen who enrolled in an undergraduate program in 2017, 2018, or 2019. We used the name generator method to determine how quantitative friendship network variables predicted the academic retention up to the first semester of 2020. Cox proportional hazard model analysis showed that the weighted indegree centrality with intimacy positively predicted retention. The results of this study can be used to identify and conduct interventions for students who may be likely to disenroll. However all of the students did not participate in the department, it was difficult to examine their entire peer networks. Thus, this study's results cannot be generalized because the participants are students of a specific major, so further research is needed to produce more generalizable results.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.