• Journal of Information Technology Services
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• v.13 no.4
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• pp.377-396
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• 2014

There is a phenomenon that the consumers from all over the countries are using the Korean online shopping malls, which has settled as a distribution industry. To get the attention of the consumers from other countries, the marketing strategy is important in order for the online shopping malls to go advance abroad. The purpose of this study is to overview the Pinterest, which is a shopping-oriented Social Network Service (SNS) based on the image content. The Pinterest is a valuable online shopping mall marketing channel. By looking at the background growth and feature of service, the existence of the mega SNS as a center in the social business is to see how Pinterest is targeting a niche market. As a result of this study, Pinterest has the advantage against other SNS for joining with online shopping malls because a tendency of the traffic referrals from Pinterest is increasing and the average of the cost of the purchase is high. Therefore, this study is reflecting various cases of utilizing the online shopping malls. In conclusion, Pinterest represents an illustrative case of effective marketing channel of the online shopping malls.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.137-145
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• 2016

The explosive growth of social networking services based on smart devices popularize these relationships and activities online in accordance with the far larger impact of this on the real life offline, the interest and importance for the online activity is increasing. In this study, factors affecting the SNS activity are defined by object, user, influence direction, influence distance and proposed a method to measure organic terms in effect between the SNS users. Influence Direction and Influence Strength (or Distance) are elaborated by using the existing influence measurement element such as structured data - the number of friends, the difference between the number of contacts - and the new influence measurement element such as unstructured data - gap between the former time and the latter time, preference and type of response behavior - that occur in social network service. In addition, the system for collecting and analysing data for measuring influence from social network service and the process model on the method for measuring influence is tested by using sample data on Facebook and explained the implementation probability.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.129-134
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• 2015

Since automatic social engineering based spam attacks induce for users to click or receive the short message service (SMS), e-mail, site address and make a relationship with an unknown friend, it is very easy for them to active in online social networks. The previous spam detection schemes only apply manual filtering of the system managers or labeling classifications regardless of the features of social networks. In this paper, we propose the spam detection metric after reflecting on a couple of features of social networks followed by analysis of real social network data set, Twitter spam. In addition, we provide the online social networks based unsupervised scheme for automated social engineering spam with self organizing map (SOM). Through the performance evaluation, we show the detection accuracy up to 90% and the possibility of real time training for the spam detection without the manager.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1087-1094
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• 2009

As online community was revitalized, the internet became the second space for people's everyday life. People enter into a connection with other on-line members and they maintain and extend their relationships. Such relationships can be analyzed and visualized with social network analysis. The method oftentimes envisions the structural elements of complex social life. The study aims at visualizing the relationships among the Cyworld users and designs an application "Blow Blow Your Pinwheel", the main purpose of this application is visualizing social relationships between ego and '1chons' which is a concept of friendship in Cyworld. Designing such an application, the study focuses on closeness of relationships which we think is composed of 1)proximity 2)similarity, 3)familiarity, and 4)reciprocity. The study used these concepts in measuring the strength of relationship between ego and other 1chons(friends). Specifically, we devised survey questionnaires which asked users to evaluate the importance of the above factors of closeness, and implemented the result in calculating the strength of the relationship between ego and other by giving weights for each factor. These measurements then were applied in visualizing the relationships in the application, we designed. Through the application, we can compare on-line relationships with off-line relationships and attempt for the new approach of Social Networks.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.675-680
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• 2016

The development of various mobile devices and mobile platform technology has led to a steady increase in the number of online social network (OSN) users. OSN users are free to communicate and share information through activities such as social networking, but this causes a new, user privacy issue. Various distributed OSN architectures are introduced to address the user privacy concern, however, users do not obtain technically perfect control over their data. In this study, the control rights of OSN user are maintained by using personal data storage (PDS). We propose a technique to improve data privacy protection that involves making a group with the user's friend by generating and providing fake text data based on user's real text data. Fake text data is generated based on the user's word sensitivity value, so that the user's friends can receive the user's differential data. As a result, we propose a system architecture that solves possible problems in the tradeoff between service utility and user privacy in OSN.

• 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.

• International Journal of Contents
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• v.15 no.2
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• pp.29-37
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• 2019

This study investigates how adolescents' smartphone attachment, social anxiety, and offline and smartphone network sizes are related to their friendship in regards to maintaining either a strong or weak bond. Based on an online survey involving 402 adolescent smartphone users in South Korea, this study found that smartphone attachment was positively related to a strong ties friendship maintenance and negatively related to weak ties friendships. Similarly, social anxiety was found to be negatively associated with friendship maintenance for both strong and weak - tie relationships. More importantly, this study revealed that the types and size of social networks moderated the relationships among adolescents with smartphone attachment, social anxiety, and friendship maintenance through smartphones.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.163-177
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• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.109-120
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• 2021

Despite the growing online exposure of retailes, offline retail channels still outperform online channels in the total retail volume of some countries. There is much interest in the physical layout plans of retail stores to expand sales. Product placement that have a large impact on customer purchasing behavior at offline retailers influences customer movement and sales volume. But in many cases, each retailer relies on unsystematic and autonomous product placement. When multiple products are sold with one purchase, the customer's movement for shopping may be evaluated in terms of customer efficiency and additional impulse purchase. In this paper, the social network is applied to sales data of a retail store and the result is used for evaluation of product placement and customer path. The frequent sales product composition was identified using k-core from sales data in the form of shopping baskets. The location was checked for the identified compositions of products, the spatial variance was measured and the customer's path was identified. With these results, the store arrangement of products was evaluated with appropriate improvement directions. The analysis method of this paper can be an alternative analysis approach for better layout of retail stores.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.