• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.117-127
• /
• 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 the Korean Society for Library and Information Science
• /
• v.50 no.3
• /
• pp.383-402
• /
• 2016

The purpose of this study is to study library marketing research trends in Korea and other countries through the analysis of author keyword network of peer-reviewed journal articles. The author keyword was collected from four major LIS journals in Korea and Scopus academic database for other countries'. The data was analyzed using NetMiner4 software. The results of the study were as follows: 1) In Korea, lots of library marketing studies focused on public libraries. However, there was a range of library marketing researches focused on academic libraries in other countries, 2) In Korea, there was not a variety of subjects of library marketing studies and the studies were mainly led by a few scholars, 3) In other countries, many scholars paid attention to digital library marketing through social media and/or web, and 4) there little library marketing studies focused on school libraries both in Korea and other countries.

• Journal of Digital Convergence
• /
• v.12 no.7
• /
• pp.177-187
• /
• 2014

In this paper, representative social network services(SNS) of Korea, China, and America such as Cyworld, Renren and Facebook were analyzed in regards to their business strategies and successful factors. From the analysis results, it was found that their common business strategies are strengthening a sense of belonging by providing differentiated customer services and by increasing customer satisfaction and this kind of strategies were naturally connected to the corporate marketing. So, Maintaining their own differentiated services, SNS companies should make a constant efforts to meet customers' needs on the base of success factors. Companies which will carry out marketing with SNS should also improve their images through communication with customers, utilizing services provided by SNS companies which match well with companies using SNS as a means of marketing. As various extensive researches are being conducted on SNS which enabled online networking, such findings will be usefully referred for similar researches and will provide theoretical basis and practical guidelines for future researches.

• The Korean Journal of Franchise Management
• /
• v.2 no.2
• /
• pp.24-44
• /
• 2011

Many companies are increasingly using social network service(SNS) as an online marketing tool, and its marketing activation has been in the limelight as a differentiation strategy most recently. The purpose of this study is to analyze online marketing cases utilizing SNS and to apply it in Franchise Enterprise in order to activate its marketing activities. This study is more concerned with the cases of facebook, twitter, and blog among social network services and suggests some ways of utilizing them in Franchise Enterprise as follows: Based on the examples of facebook, firstly, we set up the role as a homepage in individul, Franchise Enterprise, and other organizations. Secondly, we also set up the role as an organizing tool in communities, and thirdly, setting up the role as a location map tool. Regarding some applications in marketing tool of Franchise Enterprise, we suggest the role as a public relation tool of the company and brand, and also propose the role of brand planning and development. Finally, we suggest a way of overcoming the limitation in offline operations.

• The Journal of Society for e-Business Studies
• /
• v.21 no.4
• /
• pp.55-67
• /
• 2016

Global courier companies have been devoting to get more customers and profits with different service because of the worse profits from price competition. So, the effort of improving satisfaction of customers through improving courier service qualities is more important than any other time. However, the previous way to measure courier service has limitation that costs lots of time and money from off-line survey. This limitation could be overcome with less effort and costs if utilizing on-line social big data analysis and it is so helpful to improve competitiveness of courier companies. Therefore, I have collected comments from domestic and international courier companies from big data on social network service, analyzed the satisfaction of customers by R and verified the result by comparing with American Customer Satisfaction Index (ACSI) and Korea National Customer Index (NCSI) in this research. I found out the result depicts clear correlation between SNS analysis and customer satisfaction. This study can be the foundation to predict customer satisfaction easily by utilizing real time SNS information.

• Journal of Digital Convergence
• /
• v.12 no.8
• /
• pp.67-76
• /
• 2014

In this study, we investigated opinion leaders' communication activities on Facebook and analyzed the differences of communication patterns on Facebook between Korean and US college students. As a primary source of information, we conducted an online survey to collect data from students currently enrolled at two different universities in US. Additionally, we utilized online survey data previously collected from Korean students. According to our analysis, we found that US male students had more active opinion leadership than Korean male students. Also, opinion leadership of Korean students' was significantly associated with both active and passive communication patterns on Facebook whereas opinion leadership of US students' was significantly associated with passive communication patterns.

• Journal of Digital Convergence
• /
• v.11 no.12
• /
• pp.747-759
• /
• 2013

This study posits the activities of the fandom as information behavior: information producing, information diffusion, information sharing. The authors identify the underlying motivation and needs which lead fandom to these behavior on the basis of contents theory. In particular, the manner in which the those needs influence the information behavior of fandom is explored. The data used in this study came from various Fan Caf$\acute{e}$s which are communities of star fan. The results from the survey shows that the fandom is a kind of culture closely connected to social needs. Fandom can be viewed as an important driving force in the entertainment industry as they are co-creating the value in entertainment market.

• Journal of Digital Convergence
• /
• v.13 no.9
• /
• pp.115-128
• /
• 2015

As generation of Web 2.0 comes in, enormous information of corporation from various platform are being produced. However, corporations should understand features of each platform and appropriate strategies in order to attract the public in the midst of such flood of information. Numerous studies have been conducted regarding SNS which has grown rapidly in recent but a study relating a specific medium is relatively in short. So this study analyzed how information of Instagram bulletin board is accepted in perspective of consumer in Korean and America, We examined the relationship between intention of acceptance and Word Of Mouth effect through meditating effect of information usefulness. To answer the research question, we conducted online survey with Korean and USA college students. The result showed that usefulness of the information was shown to the major intermediary variable between the information characteristics of bulletin board and the intention of acceptance intention and Word Of Mouth(WOM).

• The Journal of the Korea Contents Association
• /
• v.16 no.5
• /
• pp.623-637
• /
• 2016

This study examined the importance of social capital in facilitating older adults' learning and adaptation of information technology as well as alleviating depressive symptoms. At two senior community centers in South Korea, 144 adults aged 60 and older were recruited to participate in 12 week-long technology classes to learn computers, smart phone, and internet skills. At the baseline interviews were conducted to assess their health status, depression, and online social relationships. Online and offline social capital (bonding vs. bridging) was assessed (Williams, 2006). Four-step Hierarchical Linear Regression analysis was conducted to examine the effects of online social relationship on depression. Findings suggested that depressive symptoms were associated with being widowed, being unemployed, and perceiving poor health status. Adding social capital variables in the final step, older adults who perceived less stressors, greater level of subjective health and high online bonding capitals had less depressive symptoms. Only online social bonding was significant in alleviating depression. This final model explained 48% of the variance. Computer/Internet training for older adults need to consider the significant role bonding social capital can play. The findings of this pilot study provided a preliminary base of knowledge about acceptable community-based interventions for older adults.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.731-737
• /
• 2018

The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.