• Journal of the Korean Society of School Health
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• v.34 no.2
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• pp.98-106
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• 2021

Purpose: The purpose of this study was to compare the happiness of otaku college students to that of non-otaku college students. Methods: Data were collected using structured questionnaires targeting college students who visited the S Fair, which displayed and promoted contents related to animation, figures, comics, music, entertainers, etc. A total of 236 college students were included in the analysis. Descriptive statistics, t-test, ANOVA, and multiple regression analyses were carried out using SPSS 24.0. Results: As a result of the study, 71 students (30.1%) were otaku. The happiness score was 43.17±8.62 for otaku, and 40.21±10.44 for non-otaku. After controlling for significant covariates (age, major, economic status, job seeking stress, depression, life stress, social support, and self-esteem), otaku students had a significantly higher happiness score than non-otaku students (b=1.91, p=.043). Conclusion: It was found that otaku college students were happier than non-otaku students, even though the difference was not big. Therefore, this suggests that otaku activities may, to some extent, contribute to subjective mental health such as happines.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.389-391
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• 2023

In the era of Big Data, humanity is facing a huge overflow of information. To overcome such an obstacle, many new cutting-edge technologies are being introduced. The movie recommendation system is also one such technology. To date, many theoretical and practical kinds of research have been conducted. Our research also focuses on the movie recommendation system by implementing methods from Social Network Analysis(SNA) and Parallel Programming. We applied the Girvan-Newman algorithm to detect communities of users, and a future package to perform the parallelization. This approach not only tries to improve the accuracy of the system but also accelerates the execution time. To do our experiment, we used the MovieLense Dataset.

• Journal of Technology Innovation
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• v.30 no.1
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• pp.1-20
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• 2022

Digital transformation refers to the economic and social effects of digitisation and digitalisation. Although digital transformation acts as a useful tool for economic/social development and enhancing the convenience of life, it can have negative effects (misuse of personal information, ethical problems, deepening social gaps, etc.). The government is actively establishing policies to promote digital transformation to secure competitiveness and technological hegemony, however, understanding of digital transformation-related risk issues and implementing policies to prevent them are relatively slow. Thus, this study systematically identifies risk issues of the future society that can be caused by digital transformation based on quantitative analysis of media articles big data through the Embedded Topic Modeling method. Specifically, first, detailed issues of negative effects of digital transformation in major countries were identified. Then detailed issues of negative effects of artificial intelligence in major countries and Korea were identified. Further, by synthesizing the results, future direction of the government's digital transformation policies for responding the negative effects was proposed. The policy implications are as follows. First, since the negative effects of digital transformation does not only affect technological fields but also affect the overall society, such as national security, social issues, and fairness issues. Therefore, the government should not only promote the positive functions of digital transformation, but also prepare policies to counter the negative functions of digital transformation. Second, the detailed issues of future social risks of digital transformation appear differently depending on contexts, so the government should establish a policy to respond to the negative effects of digital transformation in consideration of the national and social context. Third, the government should set a major direction for responding negative effects of digital transformation to minimize confusion among stakeholders, and prepare effective policy measures.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.536-548
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• 2018

Public data has been transformed from provider-oriented information disclosure to a form of personalized information sharing centered on individual citizens since government 3.0. As a result, the government is implementing policies and projects to maximize the value of public data and increase reuse. This study analyzes the issues related to public data in the news and seeks the status of government agencies and government projects by issue. We conducted semantic analysis on domestic online news and public agency bidding information including public data and conducted the work of linking major key words derived with social and economic values inherent in public data. As a result, major issues related to public data were divided into broader access to public data, growth of new technology, cooperation and conflict among stakeholders, and utilization of the private sector, which were closely related to transparency, efficiency, participation, and innovation mechanisms. Also major agencies of four issues include the Ministry of Strategy and Finance and Seoul, Ministry of Culture, Sports and Tourism and Gyeonggi-do, Ministry of Trade, Industry and Energy and Incheon, and Ministry of Land, Infrastructure and Transport and Gyeongsangbuk-do. Most of the issues are being led by the government.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.4
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• pp.104-117
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• 2017

This study compares the results of landscape image analysis using text mining techniques and factor analysis for Lotte World Tower, which is the first atypical skyscraper building in Korea, and identifies landscape images of the site to determine possibilities of use. Lotte World Tower's landscape image has been extracted from text mining analysis focusing on adjectives such as 'new', 'transformational', 'unusual', 'novelty', 'impressive', and 'unique', and phrases such as in the process of change, people's active elements(caliber, outing, project, night view), media(newspaper, blog), and climate(weather, season). As a result of the factor analysis, factors affecting the landscape image of Lotte World Tower were symbolic, aesthetic, and formative. Identification, which is a morphological feature, has characteristics of scale and visibility but it is not statistically significant in preference. Rather, the psychological factors such as the symbolism with characteristics such as poison and specialty, harmony with the characteristics of the surrounding environment, and beautiful aesthetic characteristics were an influence on the landscape image. The common results of the two research methods show that psychological characteristics such as factors that can represent and represent the city affect the landscape image more greatly than the morphological and physical characteristics such as location and location of the building. In addition, the text mining technique can identify nouns and adjectives corresponding to the images that people see and feel, and confirms the relationship between the derived keywords, so that it can focus the process of forming the landscape image and further the image of the city. It would appear to be a suitable method to complement the limitation of landscape research. This study is meaningful in that it confirms the possibility that big data can be utilized in landscape analysis, which is one research field of landscape architecture, and is significant for understanding the information of a big data base and contribute to enlarging the landscape research area.

• The Journal of Korean Association of Computer Education
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• v.18 no.1
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• pp.21-33
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• 2015

The purpose of this study has been found to be effective using web-based instruction than traditional teaching-learning method on academic achievement applying the meta-analysis method. The results of this study were as follows: First, The 85% subject of analysis of web-based instruction selected in this study turned out to be clear effective than traditional teaching-learning method in academic achievement of students. Second, Web-based instruction is more effective for academic achievement of elementary school students and university students than for middle school students and high school students relatively. Third, Web-based instruction is a most effective method in social subject and physical education but less effective in language subject. The overall results of this study concluded more powerful and big decisions which have integrated each different effects on academic achievement of studies web-based instruction method applying meta-analysis. Through this study, make better results were obtained and suggested the base line data and direction for follow up studies.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.71-96
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• 2023

This study aimed to investigate key research areas in the field of Library and Information Science (LIS) by analyzing trends and identifying emerging topics. To facilitate the research, a collection of 40,897 author keywords from 11,252 papers published in the past 30 years (1993-2022) in five journals was gathered. In addition, keyword analysis, as well as Principal Component Analysis (PCA) and correlation analysis were conducted, utilizing variables such as the number of articles, number of authors, ratio of co-authored papers, and cited counts. The findings of the study suggest that two topics are likely to develop as promising research areas in LIS in the future: machine learning/algorithm and research impact. Furthermore, it is anticipated that future research will focus on topics such as social media and big data, natural language processing, research trends, and research assessment, as they are expected to emerge as prominent areas of study.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.15-20
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• 2018

In modern society, crime is one of the major social problems. Crime has a great impact not only on victims but also on those around them. It is important to predict crimes before they occur and to prevent crime. Various studies have been conducted to predict crime. One of the most important factors in predicting crime is frequency of crime occurrence. The frequency of crime is widely used as basic data for predicting crime. However, the frequency of crime occurrence is announced about 2 years after the statistical processing period. In this paper, we propose a frequency analysis of crime - related key words retrieved from the web as a way to indirectly grasp the frequency of crime occurrence. The relationship between the number of frequency of crime occurrence and frequency of actual crime occurrence was analyzed by correlation coefficient.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.22-29
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• 2021

COVID-19, which has spread around the world, is also affecting local economic industries such as the domestic tourism industry and the service industry. In particular, the quality of life is threatened as safety prevention rules related to infectious diseases such as social distancing have been regularized. The purpose of this study is to analyze the impact on safety quarantine on users of the summer festival at Songho Beach in Haenam, a summer resort. In addition, it protrudes through big data surveys, demographic analysis, and technology analysis on the management of users who have changed in the COVID-19 era. It is expected to be a reference material by utilizing practical data on users in the future. In addition, this study is significant that it has been reviewed for safety and satisfaction for tourists using the summer beach festival through quarantine management using ICT technology in the COVID-19 situation, and needs to be used as good guidelines and examples for this study in the future.

• 대한공업교육학회지
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• v.40 no.1
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• pp.40-63
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• 2015

The purpose of this research is to identify internal and external factors that affect specialized technical high school students' career decision status. It then uses career decision types to predict the variables which have the impact on emotional aspects of specialized technical high school student who feel comfortable or uncomfortable after career decision in order to help specialized technical high school students choose their career. The subject of this study consist a total of 809 male and female students attending specialized technical high school located in South Korea. Data were analyzed to investigate the types of career decision status of specialized technical high school students using the SPSS 21.0 program. The results obtained through the study were as follows. Types of career decision status of specialized technical high school students were divided into four groups based on career decision status and types of emotional state. Results showed the decision-comfort(51.3%), decision-discomfort(25.6%), indecision- discomfort(15.3%), indecision-comfort(1.3%) in order. Social support were selected as variables affecting the comfort and discomfort of two groups and the determining factor which distinguished four groups. Result of the analysis showed that social support has statistically significant impact on whether it was decision or not and comfort or not. But career decision-making self-efficacy has no big impact on career decision types. Taking all results of this study together, we can see that specialized technical high school students feel comfortable and they decide the career, but still many students feel uncomfortable on their decision after deciding their career. It suggests the need of development and operation of program about emotional status of career decision which can help specialized technical high school students prepare their career comfortably after they decide on the career, and the need of segmented career counseling approach including social support group.