• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.673-682
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• 2009

Recently, photo sharing and publishing based Social Network Sites(SNSs) are increasingly attracting the attention of academic and industry researches. Unlike the face recognition environment addressed by existing works, face annotation problem under SNSs is differentiated in terms of daily updated images database, a limited number of training set and millions of users. Thus, conventional approach may not deal with these problems. In this paper, we proposed a face annotation method for sharing and publishing photographs that contain faces under a social network service using random projection, non-linear regression and representational state transfer. Our experiments on several databases show that the proposed method records an almost constant execution time with comparable accuracy of the PCA-SVM classifier.

• Journal of Information Technology Applications and Management
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• v.16 no.2
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• pp.45-63
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• 2009

China has the largest number of the user of web 2.0 such as messengers and blogs, as of 2008 year. Chinese Internet digital market will grow from $85 billion in 2007 to $264 billion in 2015. The importance of Chinese Internet business is increasing, because of its size and growth potential. This research is focused on following two questions. First, what are the major characteristics of social networking services in China? In order to find out the answer about this question, the case study on Tencent QQ and Sina Poco, the most representative social networking services company in China, was done. And the reason of the use of social networking services, their contents, and the attitude toward the paid service were identified. Second, what are the major factors influencing the use of social networking services in China. User participation, network effect, usefulness, and system quality were inferred as the factors influencing the use. These factors were based on motivation theory, technology acceptance model, and the related research papers. This research model was proved by data analysis using Partial Least Square. As results, the use of social networking services was influenced by usefulness, network effect, user participation, and system quality. But social influence was not significant of the use of social networking services.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1391-1399
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• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• Journal of KIISE
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• v.43 no.3
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• pp.380-388
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• 2016

Graph clustering is widely used to analyze a graph and identify the properties of a graph by generating clusters consisting of similar vertices. Recently, large graph data is generated in diverse applications such as Social Network Services (SNS), the World Wide Web (WWW), and telephone networks. Therefore, the importance of graph clustering algorithms that process large graph data efficiently becomes increased. In this paper, we propose an effective clustering algorithm which generates clusters for large graph data efficiently. Our proposed algorithm effectively estimates similarities between clusters in graph data using Min-Hash and constructs clusters according to the computed similarities. In our experiment with real-world data sets, we demonstrate the efficiency of our proposed algorithm by comparing with existing algorithms.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.104-111
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• 2017

The visual presentation is one key feature which gives much consideration in designing mobile applications as it acquires attention from the end user. It takes only a few milliseconds to form an impression on a person and this is not any different to the web and mobile application designs. The first few milliseconds are a crucial time for developers as the impression produced would indicate further engagement of the service. Developers should continuously update the designs based on human needs. A few of these contents have actually paved its way to being continuously used. By synthesizing results of preceding researchers, this paper considers layout, color, and font as UI design elements of SNS messenger, and illustration and animation as the graphic image of it. In this study, the preference for messaging application chat layout was being surveyed and analyzed. As a result, there has been little significance identified since the instant messaging, so chat layout shows very minimal variance in their design.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.235-255
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• 2014

Social Network Service (SNS) with emergence of Web 2.0 have been developed rapidly. Unlike other games, social network game is based on the relationship with smart devices and studied for user-centered behavior as a potential factor derived from the Technical Acceptance Model (TAM). This research is conducted for finding out influencing factors on continuous intention for smart device users. Therefore, the independent variables consist of subjective norm, mobility, playfulness, self-efficacy, and ease of use. The intermediate variables also compose of flow, and user satisfaction and the dependent variables as continuous intention. The result of study shows the following: First, subjective norm, playfulness, self-efficacy and ease of use except mobility have the positive effect on user flow. Second, subjective norm, mobility, playfulness and ease of use except self-efficacy have significantly the positive effect on user satisfaction. Finally, user flow has significantly the positive effect on user satisfaction and continuance intention and a factor of user satisfaction have significantly the positive effect on continuance intention.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.1
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• pp.41-50
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• 2023

Purpose: The aim of this descriptive study was to identify the factors influencing depression risk among South Korean nursing students. Methods: The data were collected from nursing students attending two universities through web-based questionnaires that surveyed them about depression, academic stress, social network service (SNS) addiction tendency, and upward social comparison from August 22 to September 4, 2021. The collected data from 196 nursing students were analyzed by t-test, one-way ANOVA, Pearson's correlation coefficients, and multiple linear regression. Results: The mean score of depression (using CES-D Korean version) among nursing students was 13.91, which denotes probable depression. Concerning the variance with regard to depression among nursing students, 20.2% was explained by clinical practice period, academic stress, and upward social comparison. Conclusion: Programs to relieve academic stress and depression should be developed in a simple way and systematically provided at the organizational level so that nursing students secure sufficient support during the initial and continuing period of clinical practicums. Concomitantly, an attempt to reduce the upward social comparison should be highly considered.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.281-282
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• 2021

본 논문에서는 항공 수요예측을 위한 빅데이터 기반 플랫폼의 설계 및 실증 결과를 제시한다. 항공 수요예측 통합 플랫폼은 항공산업 관련 데이터를 Open API, RSS Feed, 웹크롤러(Web Crawler) 등을 이용하여 수집 및 분석하여 자체 개발한 항공 수요예측 알고리즘을 기반으로 결과를 시각화하여 보여주도록 구현되어 있다. 또한, 제안하는 플랫폼의 사용자 인터페이스를 통해 변수 설정을 하여 단위별(Global, National 등), 기간별(단기, 중장기 등), 유형별(여객, 화물 등) 예측 통계 자료를 도출할 수 있다. 플랫폼의 성능 검증을 위해 정형화된 데이터를 비롯하여 소셜네트워크서비스(SNS), 검색엔진 등에서 수집한 비정형 데이터까지 활용하여 특정 키워드의 빈도와 특정 노선에 대한 항공 수요간 상관관계를 분석하였다. 개발한 통합 플랫폼의 지능형 항공 수요예측 알고리즘을 통해 전반적인 공항 운영 및 공항 운영 정책 수립에 기여할 것으로 예상한다.

• Informatization Policy
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• v.30 no.3
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• pp.3-28
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• 2023

This study aims to analyze the usage behavior of e-government service users based on the e-government maturity model and provide suggestions for advancement of the e-government services. The changes in Korea's e-government services were analyzed as follows; 1) Proportion of use of e-government services in Korean public services, 2) E-government service types/stages use, 3) Service use by platform 4) User response to e-government service 5) Users' requests for future e-government service usage methods. For the analysis, this study used data from Korea's 2012-2020 e-government usage behavior survey data. As a result of the analysis, first, the proportion of e-government service has been continuously increasing, and second, the use of the e-participation stage is relatively low compared to the presenting information, interaction, and transaction stages. Third, by platform, e-government service has been expanded to various access platforms such as mobile, kiosk, and SNS centering on the web. Fourth, users' satisfaction with e-government service is very high. However, to vitalize e-government services, users requested improvements such as providing one-stop integrated services and simplifying authentication procedures. Based on the analysis results, this study 1) reflects the user's point of view in the maturity model of e-government, 2) considers access to various platforms according to the development of digital technology, 3) improves the e-government maturity model through data-based analysis such as user usage behavior suggested the need.