• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.39-58
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• 2014

As the importance of big data and related technologies continues to grow in the industry, it has become highlighted to visualize results of processing and analyzing big data. Visualization of data delivers people effectiveness and clarity for understanding the result of analyzing. By the way, visualization has a role as the GUI (Graphical User Interface) that supports communications between people and analysis systems. Usually to make development and maintenance easier, these GUI parts should be loosely coupled from the parts of processing and analyzing data. And also to implement a loosely coupled architecture, it is necessary to adopt design patterns such as MVC (Model-View-Controller) which is designed for minimizing coupling between UI part and data processing part. On the other hand, big data can be classified as structured data and unstructured data. The visualization of structured data is relatively easy to unstructured data. For all that, as it has been spread out that the people utilize and analyze unstructured data, they usually develop the visualization system only for each project to overcome the limitation traditional visualization system for structured data. Furthermore, for text data which covers a huge part of unstructured data, visualization of data is more difficult. It results from the complexity of technology for analyzing text data as like linguistic analysis, text mining, social network analysis, and so on. And also those technologies are not standardized. This situation makes it more difficult to reuse the visualization system of a project to other projects. We assume that the reason is lack of commonality design of visualization system considering to expanse it to other system. In our research, we suggest a common information model for visualizing text data and propose a comprehensive and reusable framework, TexVizu, for visualizing text data. At first, we survey representative researches in text visualization era. And also we identify common elements for text visualization and common patterns among various cases of its. And then we review and analyze elements and patterns with three different viewpoints as structural viewpoint, interactive viewpoint, and semantic viewpoint. And then we design an integrated model of text data which represent elements for visualization. The structural viewpoint is for identifying structural element from various text documents as like title, author, body, and so on. The interactive viewpoint is for identifying the types of relations and interactions between text documents as like post, comment, reply and so on. The semantic viewpoint is for identifying semantic elements which extracted from analyzing text data linguistically and are represented as tags for classifying types of entity as like people, place or location, time, event and so on. After then we extract and choose common requirements for visualizing text data. The requirements are categorized as four types which are structure information, content information, relation information, trend information. Each type of requirements comprised with required visualization techniques, data and goal (what to know). These requirements are common and key requirement for design a framework which keep that a visualization system are loosely coupled from data processing or analyzing system. Finally we designed a common text visualization framework, TexVizu which is reusable and expansible for various visualization projects by collaborating with various Text Data Loader and Analytical Text Data Visualizer via common interfaces as like ITextDataLoader and IATDProvider. And also TexVisu is comprised with Analytical Text Data Model, Analytical Text Data Storage and Analytical Text Data Controller. In this framework, external components are the specifications of required interfaces for collaborating with this framework. As an experiment, we also adopt this framework into two text visualization systems as like a social opinion mining system and an online news analysis system.

• Management & Information Systems Review
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• v.35 no.4
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• pp.99-122
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• 2016

The social networking services (SNSs) have become popular among smartphone users, and one of the most popular services. In order to explain users' motivations toward SNS, this study considers uses and gratification theory which can explain individuals' motivations to select certain media channels. The purposes of this study is to investigate the relationships between motivations and addiction of SNS, and between addiction of SNS and decline in the subjective wellbeing. We examine moderating effects of social tie based on the social capital theory in the relationships between SNS addiction and decline in the subjective wellbeing. The motivations of SNS are subdivided into emotional motive (entertainment and fantasy) and cognitive motive (information share burden and challenge burden) based on the use and gratifications theory. The addiction of SNS is subdivided into time tolerance, withdrawal symptoms, interruption, and barrier of living. The data used in this study were collected from 286 SNS users through surveys. The data analysis in this study was performed using AMOS 17.0, and we used SEM(Structural Equation Modeling) methods in order to test the research model. The result shows that the emotional motive(entertainment and fantasy) and cognitive motive(information share burden and challenge burden) have an effect on the addiction of SNS. Especially emotional motive such as entertainment and users' fantasy toward SNS is an important factor that can cause SNS addiction. The addiction of SNS such as time tolerance, withdrawal symptoms, interruption, and barrier of living has an effect on the decline in the subjective wellbeing. Our result show that social tie partially moderates the relationship SNS addiction and decline in the subjective wellbeing. In addition, social tie between interruption of SNS and decline in the subjective wellbeing is an important moderating factor. The results focuses on the understanding toward relationship between SNS addiction based on the online and decline in the subjective wellbeing in the real world. The findings of this study also provides theoretical as well as practical implications which reflect the major features of SNS, and moderating effects of social tie based on the social capital.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Korean Home Economics Education Association
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• v.29 no.3
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• pp.125-140
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• 2017

The purpose of this study was to investigate the relationship of covert narcissism and the sense of alienation with SNS addiction proneness and the influence of them on SNS addiction proneness in order to find a way to conduct an intervention for the SNS addiction proneness of middle school students, and verify the mediating effect of self-esteem on the relationship between covert narcissism, the sense of alienation, and SNS addiction proneness. For this purpose, data were collected online from the first to third grade middle school students who had the experience of using SNS within the past one week, and finally, the data of a total of 341 participants were used for analysis. First, as a result of the analysis of the correlation between covert narcissism, the sense of alienation, self-esteem and SNS addiction proneness, covert narcissism and the sense of alienation showed a significant positive correlation with SNS addiction proneness. On the other hand, there was a significant negative correlation between self-esteem and SNS addiction proneness. Second, the investigation of the effects of covert narcissism, alienation, and self-esteem on SNS addiction proneness showed that covert narcissism, the sense of alienation, and self-esteem had a significant effect on the SNS addiction proneness. The higher the levels of covert narcissism and the sense of alienation, the higher the level of SNS addiction proneness. On the other hand, the higher the level of self-esteem, the lower the level of SNS addiction proneness. Third, the mediating effect of self-esteem was verified for the relationship of covert narcissism and the sense of alienation with SNS addiction proneness. Although covert narcissism and the sense of alienation may directly affect SNS addiction proneness, it was found that they had a significant effect on the SNS addiction proneness through self-esteem as a mediating variable. In other words, it was found that as the levels of the covert narcissism and the sense of alienation were increased, the level of the self-esteem was lowered, which led to a higher level of SNS addiction proneness. In order to form a proper network culture for adolescents, education on appropriate use of SNS that can be put into practice in everyday life should be conducted, and healthy and desirable SNS culture should be created. In addition, systematic education and management should be provided so that students can develop the ability to control and regulate themselves.

• Journal of Legislation Research
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• no.44
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• pp.33-62
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• 2013

In this paper, the consolidation of ICT basic legislation and ICT special legislation concerning "Ministry of Science, ICT and Future Planning" and "Korea Communications Commission" which came on the back of governmental reorganization in recent years is discussed in the theoretical and practical aspect. Development of "data communication technology" innovatively changed the method of livelihood of mankind, the emergence of network under global dimension provided financial social benefit and posed a challenge and a threat at the same time. Form digital revolution human kind can expect to receive many important blessings. Nevertheless, there are many advantages of development of technology by digital revolution, cyberspace like online media, internet etc. has realistically many problems that must be solved. To maximum positive aspects like the expansion of freedom of expression and creating plan of economy by the advance of transmission technology is needed. And to minimize side effects of informatization is required more. The First, Special Act on ICT has an adaptation in normative standardization to be fit in media convergence beyond convergence of broadcasting and telecommunications. Henceforth, there must be established a legal basis for the achievement of protection of economic evolution and freedom of speech in digital media, information, communication technology and content development. The second, the government action is to accomplish economic development and freedom of information in structural aspect of norm. Therefore minimizing normative problem by reorganization of organization remains clearly unresolved in politics. The third, Special Act on ICT must be basic law covering info-communications field, pay telecommunication and media contents field. The forth, from a technical point of view, net neutrality, conflict of interest for digital content and so on can be fixed easily. Special Act on ICT must not only pursuit of development of industry. Special Act on ICT and pursuit of enhancing quality of life of people and preparing program to promote democratization. From now on, we need to make powerful nation of information& communications technology and in information human rights protection field got to be one step ahead of others with reference to appear all the various aspects must be brought together in the discussion of legislation process of Special Act on ICT.

• Journal of Environmental Impact Assessment
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• v.28 no.5
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• pp.440-456
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• 2019

The controversy of the risk perception related to particulate matters becomes significant. Therefore, in order to understand the nature of the particulate matters, we gathered articles and comments in on-line community related to bicycling which is affected by exposure of the particulate matters. As a result, firstly, the government - led particulate matter policy was strengthened and segmented every period, butthe risk perception related to particulate matters in the bicycle community has become active and serious. Second, as a result of analyzing the perception change of outdoor activities related to particulate matters, bicycle users in community showed a tendency of outdoor activity depending on the degree of particulate matters ratherthan the weather. In addition, the level of the risk perception related to particulate matters has been moved from fears of serious threat in daily life and health, combined with the disregard of domestic particulate matter levels or mask performance. Ultimately, these risk perception related to particulate matters have led some of the bicycling that were mainly enjoyed outdoors to the indoor space. However, in comparison with outdoor bicycling enjoyed by various factors such as scenery, people, and weather, the monotonous indoor bicycling was converted into another type of indoor exercise such as fitness and yoga. In summary, it was derived from mistrust of excessive information or policy provided by the government or local governments. It is considered that environmental policy should be implemented after discussion of risk communication that can reduce the gap between public anxiety and concern so as to cope with the risk perception related to particulate matters. Therefore,this study should be provided as an academic basis for the effective communication direction when decision makers establish the policy related to particulate matters.

• The Korean Journal of Archival Studies
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• no.46
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• pp.95-152
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• 2015

"Archives of Everyday Life" refers to an organization or facility which collects, appraises, selects and preserves the document from the memory of individuals, groups, or a society through categorizing and classifying lives and cultures of ordinary people. The document includes materials such as diaries, autobiography, letters, and notes. It also covers any digital files or hypertext like posts from blogs and online communities, or photos uploaded on Social Network Services. Many research fields including the Records Management Studies has continuously claimed the necessity of collection and preservation of ordinary people's records on daily life produced every moment. Especially diary is a written record reflecting the facts experienced by an individual and his self-examination. Its originality, individuality and uniqueness are considered truly valuable as a document regardless of the era. Lately many diaries have been discovered and presented to the historical research communities, and diverse researchers in human and social studies have embarked more in-depth research on diaries, their authors, and social background of the time. Furthermore, researchers from linguistics, educational studies, and psychology analyze linguistic behaviors, status of cultural assimilation, and emotional or psychological changes of an author. In this study, we are conducting a metastudy from various research on diaries in order to reaffirm the value of "The 12th May Diaries Collection" as everyday life archives. "The 12th May Diaries Collection" consists of diaries produced and donated directly by citizens on the 12th May every year. It was only 2013 when Digital Archiving Institute in Univ. of Myungji organized the first "Annual call for the 12th May". Now more than 2,000 items were collected including hand writing diaries, digital documents, photos, audio and video files, etc. The age of participants also varies from children to senior citizens. In this study, quantitative analysis will be made on the diaries collected as well as more profound discoveries on the detailed contents of each item. It is not difficult to see stories about family and friends, school life, concerns over career path, daily life and feelings of citizens ranging all different generations, regions, and professions. Based on keyword and descriptors of each item, more comprehensive examination will be further made. Additionally this study will also provide suggestions to examine future research opportunities of these diaries for different fields such as linguistics, educational studies, historical studies or humanities considering diverse formats and contents of diaries. Finally this study will also discuss necessary tasks and challenges for "the 12th May Diaries Collection" to be continuously collected and preserved as Everyday Life Archives.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.770-788
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• 2021

This study aimed to determine whether middle school students could understand global warming and the greenhouse effect, and explain them in terms of global radiative equilibrium. From July 13 to July 24 in 2021, 118 students in the third grade of middle school, who completed a class module on 'atmosphere and weather', participated in an online assessment consisting of multiple-choice and written answers on radiative equilibrium, the greenhouse effect, and global warming; 97 complete responses were obtained. After analysis, it was found that over half the students (61.9%) correctly described the meaning of radiative equilibrium; however, their explanations frequently contained prior knowledge or specific examples outside of the presented data. The majority of the students (92.8%) knew that the greenhouse effect occurs within Earth's atmosphere, but many (32.0%) thought of the greenhouse effect as a state in which the radiative equilibrium is broken. Less than half the students (47.4%) answered correctly that radiative equilibrium occurs on both Earth and the Moon. Most of the students (69.1%) understood that atmospheric re-radiation is the cause of the greenhouse effect, but few (39.2%) answered correctly that the amount of surface radiation emitted is greater than the amount of solar radiation absorbed by the Earth's surface. In addition, about half the students (49.5%) had a good understanding of the relationship between the increase in greenhouse gases and the absorption of atmospheric gases, and the resulting reradiation to the surface. However, when asked about greenhouse gases increases, their thoughts on surface emissions were very diverse; 14.4% said they increased, 9.3% said there was no change, 7.2% said they decreased, and 18.6% gave no response. Radiation equilibrium, the greenhouse effect, and global warming are a large semantic network connected by the balance and interaction of the Earth system. This can thus serve as a conceptual system for students to understand, apply, and interpret climate change caused by global warming. Therefore, with the current climate change crisis facing mankind, sophisticated program development and classroom experiences should be provided to encourage students to think scientifically and establish scientific concepts based on accurate understanding, with follow-up studies conducted to observe the effects.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.139-150
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• 2020

The data processing of this study focuses on the textom and social media words about three areas: 'Corona 19 and professional baseball', 'Corona 19 and professional baseball', and 'Corona 19 and professional sports' The data was collected and refined in a web environment and then processed in batch, and the Ucinet6 program was used to visualize it. Specifically, the web environment was collected using Naver, Daum, and Google's channels, and was summarized into 30 words through expert meetings among the extracted words and used in the final study. 30 extracted words were visualized through a matrix, and a CONCOR analysis was performed to identify clusters of similarity and commonality of words. As a result of analysis, the clusters related to Corona 19 and Pro Baseball were composed of one central cluster and five peripheral clusters, and it was found that the contents related to the opening of professional baseball according to the corona 19 wave were mainly searched. The cluster related to Corona 19 and unrelated to professional baseball consisted of one central cluster and five peripheral clusters, and it was found that the keyword of the position of professional baseball related to the professional baseball game according to Corona 19 was mainly searched. Corona 19 and the cluster related to professional sports consisted of one central cluster and five peripheral clusters, and it was found that the keywords related to the start of professional sports according to the aftermath of Corona 19 were mainly searched.