• Information Systems Review
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• v.16 no.2
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• pp.1-23
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• 2014

With side effects such as Phishing and Spam using personal information in Social Network Service, there is a growing need for studies related to harmful behaviors such as the reason for privacy violation. As such, this study assumed privacy violation to be ethical decision, making behavior and used the Theory of Planned Behavior and Motivation Theory, which are mostly used in social science to identify the factors affecting privacy violation. The results suggested that the Perceived Enjoyment and Punishment used in motivation studies affected privacy violation behaviors, and that the factors of the Theory of Planned Behavior such as Attitude toward Privacy Violation, Subjective Norms of Privacy Violation, and Perceived Behavioral Control with regard to Privacy Violation significantly influenced the Intention to Privacy Violation. On the other hand, Perceived Curiosity and Subjective Norms of Privacy Violation did not affect the Intention to Privacy Violation. Therefore, this study confirmed that the Theory of Planned Behavior was appropriate to explain the Intention to Privacy Violation, and that the variables of the Motivation Theory generally influenced the Attitude toward Privacy Violation. This study was significant since it extended the scope of theoretical privacy study from users and victims centered to inflictor and applied the Extended Theory of Planned Behavior using the variables of the Motivation Theory in the study of Intention to Privacy Violation. From the practical aspect, it provided the ground for privacy education based on the fact that the Attitude toward Privacy Violation can be curbed when education on the Privacy Concerns, Perceived Enjoyment, and Punishment with regard to privacy is strengthened. It also cited the need for the punishment of privacy violation and the practical ground to amend the terms and conditions of user license and Personal Information Protection Act to provide policy support.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Korean Security Journal
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• no.62
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• pp.35-63
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• 2020

As the frequency and intensity of catastrophic disasters increase, there is widespread public sentiment that government capacity for disaster response and recovery is fundamentally limited, and that the involvement of civil society and the private sector is ever more vital. That is, in order to strengthen national disaster response capacity, governments need to build disaster systems that are more participatory and function through the channels of civil society, rather than continuing themselves to bear sole responsibility for these "wicked problems." With the advancement of smart mobile technology and social media, government and society as a whole have been called upon to apply these new information and communication technologies to address the current shortcomings of government-led disaster management. As illustrated in such catastrophic disasters as the 2011 Tohoku earthquake and tsunami in Japan, the 2010 Haitian earthquake, and Hurricane Katrina in the United States in 2005, the realization of participatory potential of smart technologies for better disaster response has enabled citizen participation via new smart technologies during disasters and resulted in positive impact on the management of such disasters. In this context, this study focuses on the South Korean context, and aims to analyze Korean government officials' readiness for public participation using smart technologies. On this basis, it aims to offer policy suggestions aimed at promoting smart technology-enabled citizen participation. For this purpose, it proposes a particular model, termed SMART (System, Motivation, Ability, Response, and Technology).

• Journal of The Korean Association For Science Education
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• v.37 no.2
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• pp.359-370
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• 2017

The purpose of this study is to explore the changes caused by using smart technology in school science inquiry. For this, we investigated 12 elementary school teachers' perceptions by using an open-ended questionnaire, group discussions, classroom discussions, and participant interviews. The results of this study indicate that the introduction of technology into classroom inquiry can open up the various possibilities and can cause additional burdens as well. First, teachers explained that smart technology can expand the opportunities to observe natural phenomena such as constellations and changing phases of the moon. However, some teachers insisted that, sometimes, learning how to use new devices disrupts students' concentration on the inquiry process itself. Second, teachers introduced the way of digital measurement using smart phone sensors in inquiry activities. They said that digital measurement is useful in terms of the reduction of errors and of the simplicity to measure. However, other teachers insisted that using new devices in classroom inquiry can entail additional variables and confuse the students' focus of inquiry. Communication about inquiry process can also be improved by using digital media. However, some teachers emphasized that they always talked about both the purpose of using SNS and online etiquettes with their students before using SNS. Based on these results, we discussed the necessity of additional analysis on the various ways of using digital devices depending on teachers' perceptions, the types of digital competency required in science inquiry using smart technology, and the features of norms shaped in inquiry activities using smart technology.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.2
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• pp.27-36
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• 2018

This study suggests the necessity of behavior analysis as changes to a park environment to reflect user desires can be implemented only by grasping the needs of park users. Online data (blog) were defined as the basic data of the study. After collecting data by 5 - year units, data mining was used to derive the characteristics of the time series behavior while the significance of the online data was verified through social network analysis. The results of the text mining analysis are as follows. First, primary results included 'walking', 'photography', 'riding bicycles'(inline, kickboard, etc.), and 'eating'. Second, in the early days of the collected data, active physical activity such as exercise was the main factor, but recent passive behavior such as eating, using a mobile phone, games, food and drinking coffee also appeared as a new behavior characteristic in parks. Third, the factors affecting the behavior of park users are the changes of various conditions of society such as internet development and a culture of expressing unique personalities and styles. Fourth, the special behaviors appearing at Olympic Park were derived from educational activities such as cultural activities including watching performances and history lessons. In conclusion, it has been shown that people's lifestyle changes and the behavior of a park are influenced by the changes of the various times rather than the original purpose that was intended during park planning and design. Therefore, it is necessary to create an environment tailored to users by considering the main behaviors and influencing factors of Olympic Park. Text mining used as an analytical method has the merit that past data can be collected. Therefore, it is possible to form analysis from a long-term viewpoint of behavior analysis as well as to measure new behavior and value with derived keywords. In addition, the validity of online data was verified through social network analysis to increase the legitimacy of research results. Research on more comprehensive behavior analysis should be carried out by diversifying the types of data collected later, and various methods for verifying the accuracy and reliability of large-volume data will be needed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.6
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• pp.59-68
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• 2015

It's important to analyse effects of word of mouth for making its impact higher in performance of motions pictures. And it's required to combine variety sales activities like free gift, promotion goods and price discount with word of mouth for the box office of film. The purpose of this study is to compare between groups classified by demographic characteristic into effects from word of mouth and methods of sales promotion in intention of watching film. On the other hand existing studies on sales activity and word of mouth were one-sided in theoretical background, a meaning of this study is theorizing a social phenomenon about sales promotion of movie giving actual examples that currently are effected by production company, Movie theaters, distribution company and affiliated company. For this purpose, it conducted a survey targeting 500 students in B university in Seoul city and 379 answers got received, and it proceeded this study with 369 answers except 10 inaccurate ones. Creating questionnaires with Likert 5 point scale, it decided that case of substantial inclination was 5 points and inverse one is 1 point. Doing analysis T and ANOVA according to male and female, kinds of major study and number of average monthly watching movie, it analysed the test results after comparison analysis between classified group. The results are summarized as follows: First, offering premiums is more effective by masculine than feminine, but situation of free gift is an opposite result. Second, there are no differences of effects word of mouth and methods of sales promotion by majority departments. Third, there are much differences between groups classified by average number of watching film in a month into effects from word of mouth and methods of sales promotion. Group of watching film more 3 times in a month is more effective than the other groups in intension of watching film by word of mouth. Fourth, word of mouth is great factor to increase intention of watching film and second one is discount on the price.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.139-148
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• 2020

Infectious diseases have long plagued mankind, and predicting and preventing them has been a big challenge for mankind. For this reasen, various studies have been conducted so far to predict infectious diseases. Most of the early studies relied on epidemiological data from the Centers for Disease Control and Prevention (CDC), and the problem was that the data provided by the CDC was updated only once a week, making it difficult to predict the number of real-time disease outbreaks. However, with the emergence of various Internet media due to the recent development of IT technology, studies have been conducted to predict the occurrence of infectious diseases through web data, and most of the studies we have researched have been using single Web data to predict diseases. However, disease forecasting through a single Web data has the disadvantage of having difficulty collecting large amounts of learning data and making accurate predictions through models for recent outbreaks such as "COVID-19". Thus, we would like to demonstrate through experiments that models that use multiple Web data to predict the occurrence of infectious diseases through LSTM models are more accurate than those that use single Web data and suggest models suitable for predicting infectious diseases. In this experiment, we predicted the occurrence of "Malaria" and "Epidemic-parotitis" using a single web data model and the model we propose. A total of 104 weeks of NEWS, SNS, and search query data were collected, of which 75 weeks were used as learning data and 29 weeks were used as verification data. In the experiment we predicted verification data using our proposed model and single web data, Pearson correlation coefficient for the predicted results of our proposed model showed the highest similarity at 0.94, 0.86, and RMSE was also the lowest at 0.19, 0.07.

• Journal of Life Science
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• v.18 no.3
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• pp.291-297
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• 2008

We investigated whether 1) the combined capsaicin (75 mg), sesamin (30 mg), and L-carnitine (900 mg) (CCSC) ingestion enhances autonomic nervous system (ANS) activities including thermogenic sympathetic activity as energy metabolic modulator, 2) ${\beta}_3-AR$ polymorphism of each subject influences with ANS activity. Seven healthy males $(22.0{\pm}0.5\;yr)$ volunteered for this study. The cardiac autonomic nervous activities evaluated by means of heart rate variability of power spectral analysis were continuously measured during 5 min every 30 min for total 120 min resting condition with CCSC or placebo oral administration chosen at random. The results indicated that, there are not $Arp/Arg^{64}$ variants of the ${\beta}_3-AR$ genotypes in our subjects. There were not also significant differences in heart rate during rest between both trials. The difference of ANS activity did not reach the statistical significance between both trials. However, the significant improvement showed TOTAL power, HF component, and the indices of SNS and PNS activities before and at 30 min after CCSC ingestion (p<0.05, respectively). In conclusions, although each component of combined CCSC is associated with lipolysis and/or fat oxidation, the combined CCSC consumption is not influenced in stimulation of thermogenic sympathetic activity as modulator of energy metabolism. In rather, our results suggested that CCSC ingestion improves the balance of both SNS and PNS activities. Therefore, it will be considered many combined nutrient components for ergogenic and/or lipolysis effects as well as genetic variants affecting ANS activity in further studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.

• Journal of Korean Home Economics Education Association
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• v.31 no.3
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• pp.91-116
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• 2019

The purpose of this study is to examine the meaning of Changemaker education and to investigate the significance of Changemaker education in home economics education through a study of growth of learners applying the TEMPS program. To this end, first, the concept of Changemaker education was defined. Changemaker education is an education that changes society in a positive direction through a process of thinking about, learning about, making, and participating(playing) in various problems that we face in real life and drawing out solutions and share he solutions with others. Second, in this reasearch, the direction of Changemaker education is to make them interested in social problems and solve it and to make both the family and the career life happy and healthy by collaborating with other people. The scope of the contents is defined as "the selection of the content elements of the five domains of the child family, diet nutrition, clothing, housing and consumer life". As a way of teaching, we suggested that the TEMPS phase is followed so that the session purpose is achieved. Third, the Changemaker program consists of five steps of TEMPS among the five key ideas of Changemaker education. T(Thinking) is the step of understanding the problem and thinking about how to solve it, and E(Education) is getting the background for the next step. M(Making) is a step to create a target for problem solving, and P(Participation) and P(Play) are steps to Participation and enjoy. S(Share) is a step of changing the society through the result display, SNS sharing, and class presentation. In this study, 12 programs for middle school and 15 programs for high school were developed on the basis of TEMPS level. Each of the programs consists of 2 to 12 unit hours, which add up to 68 hours in the middle school program and 68 in high school. The learners who participated in the Changemaker program for one year (March 2, 2018~December 31, 2018) will experience improvement in many aspects including the linkage of life and education, practical ability, self-directed learning, self-esteem, sense of achievement and self-reflection, sensory observation, and so on.