• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1020-1033
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• 2021

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.957-959
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• 2013

A SNS(Social Networking Service) is an online platform to build social networks or social relations among people who, for example, share free communication, information, and make more personal connections. In this paper, we find representative entities, develop relationships among them, and draw an ERD based on the entities and their relationships. And then we design a SNS database schema by converting the ERD into collections according to data model of MongoDB, which is an NoSQL database.

• International Journal of Contents
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• v.18 no.3
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• pp.21-33
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• 2022

Previous research has provided inconsistent findings as to whether socially (in)competent individuals benefit from social networking sites (SNSs) use. Based on the rich-get-richer model, some studies have shown that socially competent individuals expand their existing networks even further via SNSs use. Based on the poor-get-richer model, other studies have shown that those with poor social skills can achieve beneficiary outcomes from SNSs use by overcoming their deficient social resources of offline environments. The present study is devised to add evidence regarding how and why social skills are related to SNSs use. To this end, we tested the relationships between social competence and three types of Facebook communication activities: interaction, self-presentation, and passive observation. Further, drawing on the social cognitive theory, the mediating role of outcome expectancy in the relationship between social competence and Facebook communication activities was examined. Using an online survey in South Korea (N = 708), it was found that individuals with higher social competence were more likely than those with lower social competence to engage in interaction, self-presentation, passive observation on Facebook. Moreover, these relationships were mediated by outcome expectancy that the desired social outcomes could be achieved as a result of Facebook use.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.875-876
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• 2011

본 논문은 온라인 사회 연결망을 위한 개인정보 보호 방안들에 대해 알아보고, 각 방안이 사회 연결망의 특성 변화에 미친 영향을 분석한다. 분석 결과, 개인정보 보호 방안들은 사회 연결망의 특성을 크게 훼손시키는 것으로 나타났다

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

• 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
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• v.43 no.11
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• pp.1188-1200
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• 2016

Recommendation systems play a significant role in providing personalized information to users, with enhanced satisfaction and reduced information overload. Since the mid-1990s, many studies have been conducted on recommendation systems, but few have examined the recommendations of information from people in the online social networking environment. In this paper, we present a hybrid recommendation method that combines both the traditional system of content-based techniques to improve specialization, and the recently developed system of social network-based techniques to best overcome a few limitations of the traditional techniques, such as the cold-start problem. By suggesting a state-of-the-art method, this research will help users in online social networks view more personalized information with less effort than before.

• The Journal of Asian Finance, Economics and Business
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• v.4 no.1
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• pp.79-86
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• 2017

With the increasing trend of popularity of websites and social networking sites, it is quite evident that companies need to take cautionary measures in protecting the reputations with respect to company and brands. In this process, every company should indulge in enhancing their company and brand image through websites and social networking sites that fortify the bonding nature among them. The always-on nature of websites and social networking sites has contributed to their phenomenal marketing power and altered the balance of power between consumers and firms. Websites and social networks are used by hundreds of millions of people to communicate about a huge range of topics, including personal interests, activities, social events and even public issues. The paper explores a case study of the Ritz-Carlton hotel for their marketing strategy and organizational use of their website and social media in communicating with their customers. Even for the normal luxury traveler who would not have previously used the Internet to research a hotel or make a reservation, ritzcarlton.com is making it possible for them to do so in a sense of the luxury and typical Ritz-Carlton style. It seems to be a staple of the company for years to come.

• East Asian Journal of Business Economics (EAJBE)
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• v.11 no.1
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• pp.1-19
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• 2023

Purpose - In this study, we aimed to theorize blockchain-based social media platform users' commitment by examining the impact of their perceived justice of the token reward system. In addition, this study applied psychological ownership theory to verify the underlying mechanism between users' perceptions of justice and their commitment to the platforms. Research design, data, and methodology - To empirically test our conceptual framework in the study, we collected data through a web-based survey approach from the responses of 385 users who had experience with blockchain-based social media platforms. We employed a structural equation modeling approach to empirically test our proposed hypotheses. Result - The results indicated that distributive justice and informational justice have positive effects on user commitment. The results also showed that psychological ownership plays an important role in mediating the relationship between users' sense of distributive justice and commitment, and between procedural justice and commitment. The findings provided a better understanding of the sense of justice and user commitment in a blockchain-based social media environment. Conclusion - This study represents a preliminary attempt to theorize and empirically examine blockchain-based social media platform users' commitment. This study provided important contributions to the literature on how the effect of users' sense of justice in a reward system affects their commitment to blockchain-based social media platforms.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.3
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• pp.67-86
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• 2017

The emergence of smart devices such as smartphones (e.g., iPhone) and tablets (e.g., iPad) may enhance e-learning by increasing communication and collaborative learning outside the classroom. These devices also facilitate the use of online technologies such as Facebook. However, the adaptation of Learning Management System (LMS) services to mobile devices took longer than social networks or online tools such as Facebook and Twitter have already been long used via smartphone. The main purposes of this study are to explore students' skill levels of LMS (Moodle) and their knowledge of online tools or technologies and then examine if there is a correlation between smartphone use and using of online tools and Moodle in learning. The study conducted among 173 students in the Department of Information Studies (DIS) in Oman, using online survey. The study found that most students demonstrated high levels of accessing course/subject materials and regularly engaging with studies of using LMSs. YouTube, Wikipedia and Facebook were clearly recorded as the most popular sites among students while LinkedIn and Academia.edu were two online tools that had never been heard of by over half of the 142 participants. Emailing and searching are recorded the most popular online learning activities among students. The study concluded that students prefer to use smartphone for accessing these tools rather than using it to access LMSs, while a positive correlation was found between the use of these tools and smartphones, but there was no correlation between smartphones and using LMSs.