• Journal of Communications and Networks
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• v.17 no.3
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• pp.296-305
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• 2015

Coupled schemes between service-oriented architecture (SOA) and Web 2.0 have recently been researched. Web-based content providers and telecommunications company (Telecom) based Internet protocol television (IPTV) providers have struggled against each other to accommodate more three-screen service subscribers. Since the advent of Web 2.0, more abundant reproduced content can be circulated. However, because according to increasing device's resolution and content formats IPTV providers transcode content in advance, network bandwidth, storage and operation costs for content management systems (CMSs) are wasted. In this paper, we present a user centric CMS for open IPTV, which integrates SOA and Web 2.0. Considering content popularity based on a Zipf-like distribution to solve these problems, we analyze the performance between the user centric CMS and the conventional Web syndication system for normalized costs. Based on the user centric CMS, we implement a social Web TV with device-aware function, which can aggregate, transcode, and deploy content over social networking service independently.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.99-110
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• 2011

Mobile environments rapidly changing and digital convergence widely employed, mobile devices including smart phones have been playing a critical role that changes users' lifestyle in the areas of entertainments, businesses and information services. The various services using mobile devices are developing to meet the personal needs of users in the mobile environments. Especially, an LBS (Location-Based Service) is combined with other services and contents such as augmented reality, mobile SNS (Social Network Service), games, and searching, which can provide convenient and useful services to mobile users. In this paper, we design and implement the prototype of mobile personal assistant (PA) agents. Our personal assistant agent helps users do some tasks by hiding the complexity of difficult tasks, performing tasks on behalf of the users, and reflecting the preferences of users. To identify user's preferences and provide personalized services, clustering and classification algorithms of data mining are applied. The clusters of the log data using clustering algorithms are made by measuring the dissimilarity between two objects based on usage patterns. The classification algorithms produce user profiles within each cluster, which make it possible for PA agents to provide users with personalized services and contents. In the experiment, we measured the classification accuracy of user model clustered using clustering algorithms. It turned out that the classification accuracy using our method was increased by 17.42%, compared with that using other clustering algorithms.

• Journal of the Korea society of information convergence
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• v.6 no.2
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• pp.15-22
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• 2013

Applying iterative learning utilizing SNS & Community to the class for pre-kindergarten teachers, the effectiveness of teaching satisfaction, self-efficacy, and curriculum understanding was verified. A iterative learning model utilizing SNS & Community in teachers leading traditional off-line teaching at college education field was applied separately into thinking to one-self by advance organizer, thinking together by presentation in the beginning of the class, and sharing the thoughts by community activities after the class. Iterative learning begins by being sent SNS to students from teachers before the class, but learners for themselves subsequently start to proceed self-directed learning activities. As a result, class satisfaction and understanding of pedagogy have been increased, and it had a positive influence on self-efficacy. Thus, it is to suggest utilizable SNS of professors and a teaching method utilizing Community to college students who need basic learning skills.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.754-764
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• 2016

To understand and analyze SNS(Social Network Service) two important classes of networks, small-world and scale-free networks have gained a lot of research interests. In this study, a generalized network generation method is developed, which can produce small-world network, scale-free network, or network with the properties of both small-world and scale-free by controlling two input parameters. By tuning one parameter we can represent the small-world property and by tuning the other one we can represent both scale-free and small-world properties. For the network measures to represent small-world and scale-free properties clustering coefficient, average shortest path distance and power-law property are used. Using the model proposed in this study we can have more clear understanding about relationships between small-world network and scale-free network. Using numerical examples we have verified the effects of two parameters on clustering coefficient, average shortest path distance and power-law property. Through this investigation it can be shown that small-world network, scale-free network or both can be generated by tuning two input parameters properly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.184-192
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• 2019

Focusing on the emerging influencer market of SNS (Social Network Service), This study attempts to clarify the priority of Influencer characteristics when the customer decided to purchase products through the promotion of influencers. Since the influencer marketing differs from the Online information source marketing, this study has an academic implication in that integrated the influence of the characteristics of the Online information sources and the characteristics of SNS contents. For this purpose, through the literature research on Information sources and Influencers, the characteristics of influencers were reconstructed and priorities were derived using the AHP (Analytic Hierarchy Process) technique. The upper layer of the AHP structure was set to expertise, trustworthiness, social attractiveness, and content attractiveness, and the lower layer structured the model with 13 subfactors. The results are summarized as follows. First, in the result of combining the importance of the $1^{st}$ and $2^{nd}$ classes, the detailed factors of expertise and trustworthiness, ranked from the top to six, are largely influenced in purchasing decisions in influencer marketing. Second, content attractiveness is the third rank in the $1^{st}$ layer. Influencer marketing shows that content is more important than social attractiveness. Besides, the $7^{th}$ to $9^{th}$ positions of the overall rankings accounted for visual information, storytelling, and external attractiveness, which are the details of content attractiveness, and it is confirmed that it is more effective for influencer marketing to emphasize content attractiveness than social attractiveness. Although the influencer marketing differs from the existing information marketing, this study has an academic implication in that integrated the influence of the characteristics of the source and the characteristics of the contents.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.447-461
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• 2012

A social network service(SNS) is gaining popularity as a new real-time information sharing mechanism. However, the user's privacy infringement is occurred frequently because the information that is shared through a social network include the private information such as user's identity or lifestyle patterns. To resolve this problem, the research about privacy preserving data sharing in social network are being proceed actively. In this paper, we proposed the efficient scheme for privacy preserving data sharing in social network. The proposed scheme provides an efficient conjunctive keyword search functionality. And, users who granted access right to storage server can store and search data in storage server. Also,, our scheme provide join/revocation functionality suited to the characteristics of a dynamic social network.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.223-232
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• 2023

The purpose of this study is to determine the impact of social network sports community consciousness on loyalty and sports attitude. In order to achieve this research purpose, the population of the study was selected as adult men and women over the age of 20 who are active in the social network sports community in 2022. The sampling method used cluster random sampling to select a total of 300 people, 150 men and 150 women, as research subjects. The survey tool used was the questionnaire method, and the questionnaire whose reliability and validity had been verified in previous studies at home and abroad was used by requoting, modifying, or supplementing it to suit the purpose of this study. It was also structured on a 5-point scale. Frequency analysis, factor analysis, reliability analysis, simple regression analysis, and multiple regression analysis were performed on the collected data using the statistical program SPSS Windows 20.0 Version. The results obtained through this process are as follows. First, social network sports community consciousness was found to have a partial effect on loyalty. Second, social network sports community consciousness was found to have a partial effect on sports attitudes. Third, social network sports community loyalty was found to have a partial effect on sports attitudes. Considering these results, various activities such as decision-making process, relationship formation, and opinion expression of modern people are carried out by the O-line community. In addition, while in the past it was a format that led from offline activities to online activities, currently, there are more and more formats that lead from online activities to offline activities. Therefore, modern people's SNS sports community activities provide many experiences, which creates a sense of community and sports attitudes are formed based on this. This can be said to lead to loyal activities.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.304-306
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• 2012

최근 들어 온라인 쇼핑몰과 SNS(Social Network Service)가 결합한 새로운 쇼핑방식인 소셜쇼핑(social shopping)과 소셜커머스(social commerce)가 핫이슈가 되었다. 소셜커머스는 이전에 존재하지 않던 수요를 창출해 낼 수 있다는 점에서 공동구매와는 전혀 다른 새로운 상거래방식이다. 소셜커머스는 50% 이상의 가격할인율과 특정 거래조건(품목, 거래가능 시간, 사용기간, 물량 등)의 제약을 통해 관심의 경제(economy of attention)를 극대화한 수익모델이라 할 수 있다. 본 연구에서는 소셜커머스에서 가격할인과 소비자들의 제품에 대한 지각된 위험은 충동구매에 어떤 영향을 미치는지에 대해서 검토하고자 하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.101-104
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• 2012

최근 소설 네트워크 서비스(Social Netwrok Services)를 활용한 소설 태깅 서비스에 대한 연구가 활발하게 진행 중이다. 특히 태그 기술을 이용한 협력적 태깅 시스템(collaborative tagging system)은 북마크, 문서, 사진, 동영상과 같은 웹 자원을 조직화하고 공유할 수 있는 수단으로 제공하고 있다. 그러나 광고 홍보 목적을 가진 스패머들은 콘텐츠와 관련 없는 태그를 달아 놓아 검색 키워드와 무관한 결과 값이 검색되어 웹 검색 서비스를 이용하는 사용자들에게 불편을 주고 있다. 따라서 본 연구에서는 콘텐츠와 태그의 연관성을 높이기 위해 태그에 일반 사용자가 유사도를 입력할 수 있는 추천 시스템을 적용하여 순위 조작 방지방법을 제안하였다.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.325-332
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• 2011

Social Network Services(SNS) such as Twitter, Facebook and Myspace have gained popularity worldwide. Especially, sentiment analysis of SNS users' sentence is very important since it is very useful in the opinion mining. In this paper, we propose a new sentiment classification method of sentences which contains formal and informal vocabulary such as emoticons, and newly coined words. Previous methods used only formal vocabulary to classify sentiments of sentences. However, these methods are not quite effective because internet users use sentences that contain informal vocabulary. In addition, we construct suggest to construct domain sentiment vocabulary because the same word may represent different sentiments in different domains. Feature vectors are extracted from the sentiment vocabulary information and classified by Support Vector Machine(SVM). Our proposed method shows good performance in classification accuracy.