• Fashion & Textile Research Journal
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• v.19 no.6
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• pp.701-711
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• 2017

This research aimed to understand selfie behavior in social networking sites (SNSs). The research was conducted on the basis of the functional theories of attitude, verified self-presentation attitude, and self-expression attitude that affect selfie behaviors (i.e., taking selfies, posting selfies, and taking selfies for fashion product exposure). The moderating effect of satisfaction toward one's appearance was identified. The participants of the study were SNS users aged 20-30 years who had posted selfies in the past month. A survey was performed using an online panel of an international survey firm. The data were analyzed using hierarchical regression analysis on SPSS 22.0. Results corroborated that self-expression attitude affected the number of selfies taken but not the number of selfies posted and those uploaded for fashion product exposure. Self-presentation attitude exerted a significant effect on the number of selfies posted and those uploaded for fashion product exposure. When satisfaction toward one's appearance was high, self-presentation attitude increased the influence of the behaviors of posting selfies and uploading selfies for fashion product exposure. Self-expression attitude also significantly influenced the number of selfies taken due to the moderating effect of satisfaction toward one's appearance. This research was made meaningful by its quantitative analysis of selfie behavior in SNSs. The results confirmed the different functions of attitudes affecting selfie behavior. With the improved understanding of selfie behavior obtained from this research, Social Media marketing may be carried out in various industrial fields in the future.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.81-97
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• 2020

Geopark refers to a single boundary area consisting of a collection of geosites and geotrails, which includes ecological, historical and cultural elements based on geological and geomorphological resources. To ensure the continued development and conservation of existing listed geoparks, it is necessary to carry out an environmental sensitivity analysis of the geopark components by utilizing spatial information from various scales. The objectives of this study are to analyze the environmental sensitivity in Cheongsong UNESCO global geopark in relation with slope disaster using GIS and to understand its spatial distribution in connection with geosites and geotrails. Two types of spatial database were constructed; geosites and geotrails in Cheongsong UNESCO global geopark and spatial data to perform environmental sensitivity. Potential soil loss and slope stability were analyzed to derive environmental sensitivity related to slope hazard. The results showed relatively high environmental sensitivity along the drainage network of Cheongsong UNESCO global geopark. Zonal statistics analysis was conducted for further detailed distribution of environmental sensitivity based on buffer zones of geosites and geotrails. Majority of geological sites, geological trails, Jeolgol gorge~Jusan Pond section in hiking trails, and Dalgi Mineral Spring Site~Artistic Genius Republic of Korea(Jangnankki gonghwaguk) section in road areas show relatively high slope hazard sensitivity within buffer zones.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.305-313
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• 2021

This study collected and analyzed big data from 1995 to 2020, focusing on the keywords "unwed mother", "single mother," and "single mom" to present appropriate government support policy directions according to changes in perspectives on unwed mothers. Big data collection platform Textom was used to collect data from portal search sites Naver and Daum and refine data. The final refined data were word frequency analysis, TF-IDF analysis, an N-gram analysis provided by Textom. In addition, Network analysis and CONCOR analysis were conducted through the UCINET6 program. As a result of the study, similar words appeared in word frequency analysis and TF-IDF analysis, but they differed by year. In the N-gram analysis, there were similarities in word appearance, but there were many differences in frequency and form of words appearing in series. As a result of CONCOR analysis, it was found that different clusters were formed by year. This study confirms the change in the perspective of unwed mothers through big data analysis, suggests the need for unwed mothers policies for various options for independent women, and policies that embrace pregnancy, childbirth, and parenting without discrimination within the new family form.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Korean Journal of Health Education and Promotion
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• v.30 no.3
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• pp.59-73
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• 2013

Objectives: The study is aimed at examining the individual reasons and regional/environmental factors of online search on suicide using social big data to predict practical behaviors related to suicide and to develop an online suicide prevention system on the governmental level. Methods: The study was conducted using suicide-related social big data collected from online news sites, blogs, caf$\acute{e}$s, social network services and message boards between January 1 and December 31, 2011 (321,506 buzzes from users assumed as adults and 67,742 buzzes from those assumed as teenagers). Technical analysis and development of the suicide search prediction model were done using SPSS 20.0, and the structural model, nd multi-group analysis was made using AMOS 20.0. Also, HLM 7.0 was applied for the multilevel model analysis of the determinants of search on suicide by teenagers. Results: A summary of the results of multivariate analysis is as follows. First, search on suicide by adults appeared to increase on days when there were higher number of suicide incidents, higher number of search on drinking, higher divorce rate, lower birth rate and higher average humidity. Second, search on suicide by teenagers rose on days when there were higher number of teenage suicide incidents, higher number of search on stress or drinking and less fine dust particles. Third, the comparison of the results of the structural equation model analysis of search on suicide by adults and teenagers showed that teenagers were more likely to proceed from search on stress to search on sports, drinking and suicide, while adults significantly tended to move from search on drinking to search on suicide. Fourth, the result of the multilevel model analysis of determinants of search on suicide by teenagers showed that monthly teenagers suicide rate and average humidity had positive effect on the amount of search on suicide. Conclusions: The study shows that both adults and teenagers are influenced by various reasons to experience stress and search on suicide on the Internet. Therefore, we need to develop diverse school-level programs that can help relieve teenagers of stress and workplace-level programs to get rid of the work-related stress of adults.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.255-260
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• 2020

In this paper, I propose an efficient brand identity strategy by topic modeling the Instagram posts, one of SNS(Social Network Service) having more than 1billion world-wide and 500 million daily users. Since the 92% age groups of the Instagram is 18~50 years old (59% 18~29y and 33% 30~49), I set research analysis target three mobile commerce sites to dress and cosmetics sales sites that sale apparels cosmetics and gadgets that recently opened and have operated marketing on diverse channel including SNS. By topic modeling SNS posts for 6 months after launching the site that tagged each m-commerce site brand name or company name, I validate companies' brand identity strategy works effectively and suggest moderation of strategy for brand image. As a result, I found one of three mobile commerce site has different brand image by users and need different identity set up.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1353-1360
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• 2015

With the extreme development of Internet, recently most users refer the sites with the various Recommendation Systems (RSs) when they want to buy some stuff, movie and music. However, the possibilities of the Sybils with the malicious behaviors may exists in these RSs sites in which Sybils intentionally increase or decrease the rating values. The RSs cannot play an accurate role of the proper recommendations to the general normal users. In this paper, we divide the given rating values into the stable or unstable states and propose a system information based recommendation algorithm that minimizes the malicious user's influence. To evaluate the performance of the proposed scheme, we directly crawl the real trace data from the famous movie site and analyze the performance. After that, we showed proposed scheme performs well compared to existing algorithms.

• Korean Journal of Construction Engineering and Management
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• v.19 no.5
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• pp.101-110
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• 2018

Safety-related accidents are continuously increasing in the construction field. In order to prevent or minimize such accidents, a systematic and active safety activities of on-site managers and workers are required. However, preexisting advance research is lacking on how the safety organization systems are built in construction sites varying in sizes, and how well-trained the related personnel are in safety knowledge and task performances. Especially, a research on the analysis of the formation of their safety knowledge sharing and safety performance cooperation and their visualization is virtually nonexistent. In this regard, this research was conducted with a purpose to study the generalized safety organization method of domestic construction sites, analyze the safety knowledge and cooperation system of the members based on the SNA method and ultimately provide a hands-on data that could be effectively utilized in building an effective safety management system in the future.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.11-28
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• 2021

As untact travel has become a new trend in leisure culture due to the spread of COVID-19, car camping market is rapidly increasing. The sales of car camping-related goods increased by up to 600 percent, and the sales of SUV in Korea also increased by about four times. Despite the growth of the car camping market, there is a lack of research on the actual condition of the car camping market or research on the user's perspective. Therefore, in this study, a survey of actual camping users was conducted to derive factors that they consider important in camping, and through this, a car camping map was produced. As a result, two types of maps were produced: a map about the car camping site and convenience facilities closest to the car camping site in Gangwon-do, and a hash tag themed map based on keywords for each car camping site. We gathered data on portal sites and social media to obtain information related to camping sites and proceeded with analysis using text mining. In addition, we extracted keywords using network analysis techniques and selected key themes that represent them. This allows the user to choose a car camping site by selecting keywords that suit their taste. We hope that this research will help car camping researchers as a prior study and provide a foundation for leading a clean camping culture through clean camping campaign. Also, we hope that car camping users will be able to do quality trip.