• Journal of Fisheries and Marine Sciences Education
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• v.27 no.1
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• pp.108-114
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• 2015

The purpose of this study is to find the relationships among communication competency, social network centrality(trust centrality and knowledge sharing centrality), discussion performance, and online boarding activity in the team based learning situation. For investigating this topic, 44 students are participated in the classes of educational technology. In order to find out the relationships among communication competency, social network centrality, discussion performance, and online boarding activity, compared t-test and path analysis are used. Followings are the results of the research: (a) Communication competency is improved significantly after team based learning. (b) Trust centrality effects significantly on the knowledge sharing centrality. (c) Knowledge sharing effects significantly on discussion performance. (d) Trust centrality effects on the online boarding activity in the team based learning.

• International Journal of Contents
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• v.10 no.4
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• pp.18-22
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• 2014

Social network games (SNGs), a rapidly growing online game genre, are built and played on social network sites. SNGs provide an online world for enjoying leisure time and interpersonal communication, and an increasing numbers of college students are involved in such game-playing as a leisure time activity. Despite the popularity, relatively few studies have been conducted to investigate the nature of game players, especially the meaning of such leisure time behavior by college students. This paper's aim was to explore a subjective meaning structure of online social network game play. The means-end chain model was used to link attributes of SNGs to the underlying values of game playing as a leisure activity. The results revealed two emerging end-values: the need for bridging and a sense of belonging. This study sheds light on the meaning of college students' leisure activities when playing social network games.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.370-379
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• 2012

This study is identifying a social standing on adolescents' social network in offline and how the social standing influence to online social activity. For the purpose, we explore two research questions. First, How the adolescents' social standing present in their offline social network? Second, How the adolescents' social standing influence to online social activity? Using data, we first visualized 5 social network of adolescents, and deducted each ego networks and global network. Also we investigated causality between social standing and social activities. The result showed adolescents' social tie and social gregariousness influence to social activity width and depth in ego network. Based on these findings, we discussed some implications, limitations, and future direction.

• The Journal of Information Systems
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• v.29 no.2
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• pp.221-242
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• 2020

Purpose This study collects data of the recently activated online black market and analyzes it to present a specific method for preparing for a hacking attack. This study aims to make safe from the cyber attacks, including hacking, from the perspective of individuals and businesses by closely analyzing hacking methods and tools in a situation where they are easily shared. Design/methodology/approach To prepare for the hacking attack through the online black market, this study uses the routine activity theory to identify the opportunity factors of the hacking attack. Based on this, text mining and social network techniques are applied to reveal the most dangerous areas of security. It finds out suitable targets in routine activity theory through text mining techniques and motivated offenders through social network analysis. Lastly, the absence of guardians and the parts required by guardians are extracted using both analysis techniques simultaneously. Findings As a result of text mining, there was a large supply of hacking gift cards, and the demand to attack sites such as Amazon and Netflix was very high. In addition, interest in accounts and combos was in high demand and supply. As a result of social network analysis, users who actively share hacking information and tools can be identified. When these two analyzes were synthesized, it was found that specialized managers are required in the areas of proxy, maker and many managers are required for the buyer network, and skilled managers are required for the seller network.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.404-414
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• 2017

With the development of smartphones and online social networks, users produce a lot of contents and share them with each other. Therefore, users spend time by viewing or receiving the contents they do not want. In order to solve such problems, schemes for recommending useful contents have been actively studied. In this paper, we propose a contents recommendation scheme using collaborative filtering for users on online social networks. The proposed scheme consider a user trust in order to remove user data that lower the accuracy of recommendation. The user trust is derived by analyzing the user activity of online social network. For evaluating the user trust from various points of view, we collect user activities that have not been used in conventional techniques. It is shown through performance evaluation that the proposed scheme outperforms the existing scheme.

• Knowledge Management Research
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• v.15 no.2
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• pp.107-128
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• 2014

From old times, companies have created profits by developing business models utilizing their friendship with customers, so the relationships can be connected to the activity giving a gift. This activity of giving a gift is developed as the service of giving gifts via social network services (SNS) as the use of SNS and smart phones is recently increased in relation to that, this study set the online friendship desire and the SNS gift convenience as the intrinsic motive and the extrinsic motive of the SNS gift behavior, respectively. This study identified how the interactivity's sub factors influenced on the online friendship desire/SNS gift convenience by reorganizing the interactivity's sub factors in the mobile context. As the results of this study, it was found that the connectedness, the synchronicity and playfulness positively influenced on the online friendship desire for the SNS gift convenience, the only connectedness positively influenced on it. And it was identified that the SNS gift convenience and the online friendship desire positively influenced on the SNS gift intention. This study is academically meaningful in that it conducted an empirical research by focusing on the friendship desire in relation to the SNS gift. Besides, through the results of this study, the online friendship desire and the SNS gift convenience will have to be considered as providing any SNS gift service, and that is expected to create knowledge for SNS business model to companies.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.96-104
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• 2020

As consumers' consumption activities become more active due to the activation of online shopping malls, companies are conducting item trend analyses to boost sales. The existing item trend analysis methods are analyzed by considering only the activities of users in online shopping mall services, making it difficult to identify trends for new items without purchasing history. In this paper, we propose a trend analysis method that combines data in online shopping mall services and social network data to analyze item trends in users and potential customers in shopping malls. The proposed method uses the user's activity logs for in-service data and utilizes hot topics through word set extraction from social network data set to reflect potential users' interests. Finally, the item trend change is detected over time by utilizing the item index and the number of mentions in the social network. We show the superiority of the proposed method through performance evaluations using social network data.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.23-34
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• 2019

The purpose of this study was to examine the relationship between the access level of digital information service and life satisfaction level of the middle and high-aged people and to analyze the Sequential Mediation Effects on online social engagement and online network activities. To this end, we analyzed the effects of multiple mediations on 1,491 seniors who responded to the 2018 digital information gap survey. The results of the study are as follows: First, this study confirmed that there is a statistically significant relationship between the access levels of digital information service and the life satisfaction. Second, the results showed that impact of digital information access level on life satisfaction among high-aged people was higher when they were engaged in both online social activities and online networking, rather than only involved in online social activities. Overall, this study comprehensively examined the relationship among the level of digital information access, life satisfaction, online social engagement, and online networking, which is meaningful in that it can be used as data for reconsideration of the digital information services and life satisfaction of the high-aged people.

• 한국노년학
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• v.40 no.1
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• pp.131-146
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• 2020

The objective of this study is to examine the relationship between attitude in digital usage and life satisfaction level of the middle-aged people and older adults, and to analyze Sequential Mediation Effects of online networking activity and information producing and sharing in the online context. To achieve the main objectives, we conducted Hayeys'(2013) Process for SPSS Macro. The followings are the results of the study: First, there is a strong relationship between the attitude towards digital usage and the life satisfaction. Second, the results showed that impact of attitude in digital usage on life satisfaction among the older people is 0.291 unit higher, when they are engaged both in online networking activity and digital information production/sharing activities compared to involved in online networking activity alone. The results of the study is meaningful in that they can be used as a baseline data for reconsideration of digital usage and life satisfaction of the older adults, by providing comprehensive examination of relationship among attitude in digital usage, life satisfaction, online network activities, and digital information production·sharing activities of the older adults.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.