• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.68-79
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• 2021

Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

• Korean journal of communication and information
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• v.46
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• pp.556-589
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• 2009

With the advent of the so-called Web 2.0 age, the interconnections of various contents on the web, as well as the user-participatory services from blogs, web-based communities, picture sharing sites, and social networking sites, to the sites for collective knowledge productions, have been further vitalized. As the User Generated Contents(UGCs) are flourishing on the web, they have channeled users' desires for self-expression and social acknowledgement, and yet have created the new kinds of invasion of privacy. This study attempts to look at how the networked individuals' everyday perceptions of privacy have been reconstructed in the age of Web 2.0. By investigating how users have used the UGCs for their sociality on the web and how they have set the boundaries of the private and the public in these public or semi-public disclosures of self-expressions, it has traced the changing perceptions of privacy in everyday communication practices. For this study, it has interviewed Korean youngsters in their 10s and 20s who have grown up with the Internet and have received self-expressions and social communication on the web as everyday activities. Based on their interviews, it inquires into the concurrent notion of privacy and discuss its cultural implications.

• 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.

• Fashion & Textile Research Journal
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• v.15 no.6
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• pp.971-976
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• 2013

This study provides insight into current social media influences and purchasing power of the young generation in that the size of both of these demographic groups will impact the apparel companies and retail market for the predictable future Denim apparel companies are aware of the discretionary spending power of the Y and Z Generations. The characteristics of current teens are so similar to college-age individuals in that they have grown up with digital technology and they prefer to communicate via social networking sites. Retailers have utilized these social media platforms in order to capture the attention of the generations. Traditionally marketing campaigns have differentiated between teens and the college-age population. However, the teens actually have larger spending power and more discretionary income. A survey consisted of 32 questions pertaining to Internet media influences, influence of people, and decision factors on decisionmaking related to purchasing selection. A random sampling of 163 females responded to a set of questionnaires. Teens, like college students desire to make their own decisions when they select and purchase denim jeans. Overall 40% of them wanted to make their own decisions when purchasing their jeans, however, a significant number are influenced by their friend's opinions (34%) and the opinions of family members (15%). However, celebrities (10%) had the least influence on their decisions. Teens, like colleges students make decisions based on the same decision factors: fit (63%), cost (23%), brand (10%) and color (2%). The most important factor in determining preference was "fit".

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.97-106
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• 2023

Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1187-1196
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• 2019

Information security has become a major challenge with the advent of cloud and social networking sites. Conventional encryption algorithms might not be suitable for image encryption because of the large data size and high redundancy among the raw pixels of a digital image. In this paper, we generalize the encryption method for of color image proposed by Jeong et al. to color image encryption using parametric 3-dimensional chaotic cat map using Laplace expansion and C-MLCA. Through rigorous experiments, we demonstrate that the proposed new image encryption system provides high security and reliability.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.267-273
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• 2020

With the strong growth of social networking sites such as Facebook in recent years, the potential of exploiting customers on Facebook is increasing. Presently, trading activities on Facebook is rapidly developing. Therefore, businesses have become increasingly competitive when selling products on Facebook, so as to retain customers as well as to satisfy customer, which is of paramount importance. This study was conducted to assess the factors affecting the satisfaction of individual customers in Vietnam when buying goods on Facebook. This study uses multivariate analysis techniques (Confirmatory Factor Analysis, Structural Equation Modeling) to determine the factors affecting customer satisfaction when buying goods on Facebook. Research results from 268 individual customers in Vietnam indicated trust and convenience are the two important factors related to customer satisfaction when buying goods on Facebook. Customer satisfaction is the result of consumer experience throughout the different stages of purchase. The more the shopping experience, the more the customers are satisfied when buying products. The price and products do not affect customer satisfaction (prices are easy to compare and products are easily understood on the Internet; hence, these two factors are not considered as determinants of customer satisfaction). Furthermore, this study provides recommendations to improve customer satisfaction.

• Korean Journal of Health Education and Promotion
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• v.25 no.4
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• pp.93-110
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• 2008

Objectives: The study aimed at reviewing the organizational values, structures, and activities of the health promotion foundation model as a recently recommended by the World Health Organization, and exploring adequate suggestions to administer the funds in Korea. Methods: The study materials were collected from web-sites and visiting, the ThaiHealth, VicHealth, Healthway, and Health Promotion Switzerland were reviewed as the representative cases of health promotion foundation model. Results: According to the review, the health promotion foundation established based on relevant legal acts had the comprehensive and professional organizational structure with boards and committees as governing and supporting bodies. The foundations had clearly defined vision, mission, and purpose, and pursuit health promotion purpose, independent and professional decision making process, strategies and priorities to initiate broad health promotion activities, balanced funds distribution to various areas and sectors, and networking and collaborating with partners. Conclusions: Health promotion foundation is a recommendable model to lead more effective and efficient health promotion activities and to collaborate with other sectors or other countries. Expanded usages of health promotion fund into the diverse health promotion settings such as communities, work places and schools and health activities including sponsorships as well as health promotion programs need to be considered.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.352-359
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• 2021

The purpose of this study is to design and build fake news discrimination systems and methods using fact-checking guidelines. In other words, the main content of this study is the system for identifying fake news using Artificial Intelligence -based Fact-checking guidelines. Specifically planned guidelines are needed to determine fake news that is prevalent these days, and the purpose of these guidelines is fact-checking. Identifying fake news immediately after seeing a huge amount of news is inefficient in handling and ineffective in handling. For this reason, we would like to design a fake news identification system using the fact-checking guidelines to create guidelines based on pattern analysis against fake news and real news data. The model will monitor the fact-checking guideline model modeled to determine the Fact-checking target within the news article and news articles shared on social networking service sites. Through this, the model is reflected in the fact-checking guideline model by analyzing news monitoring devices that select suspicious news articles based on their user responses. The core of this research model is a fake news identification device that determines the authenticity of this suspected news article. So, we propose news article identification methods with fact-checking guideline on Artificial Intelligence & Bigdata. This study will help news subscribers determine news that is unclear in its authenticity.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.17-24
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• 2018

Today, we are living in the era of data and information. With the advent of Internet of Things (IoT), the popularity of social networking sites, and the development of mobile devices, a large amount of data is being produced in diverse areas. The collection of such data generated in various area is called big data. As the importance of big data grows, there has been a growing need to share big data containing information regarding an individual entity. As big data contains sensitive information about individuals, directly releasing it for public use may violate existing privacy requirements. Thus, privacy-preserving data publishing (PPDP) has been actively studied to share big data containing personal information for public use, while preserving the privacy of the individual. K-anonymity, which is the most popular method in the area of PPDP, transforms each record in a table such that at least k records have the same values for the given quasi-identifier attributes, and thus each record is indistinguishable from other records in the same class. As the size of big data continuously getting larger, there is a growing demand for the method which can efficiently anonymize vast amount of dta. Thus, in this paper, we develop an efficient k-anonymity method by using Spark distributed framework. Experimental results show that, through the developed method, significant gains in processing time can be achieved.