• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.579-585
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• 2021

This study aims to provide an in-depth description of the practices of social media users in the Kingdom of Saudi Arabia (KSA)-specifically the users of Facebook and Snapchat-and the reasons for these practices, the decisions made, and the people involved. Qualitative methods were used to collect data in two rounds from 53 participants. The data analysis shows a clear preference for Snapchat over Facebook among the participants, as shown in their using the application many times daily and in the creation and use of new words derived from the application's name. On the other hand, one of the main reasons mentioned by the participants for not preferring Facebook was the unclear policy of security and privacy used in the application. This reason is important for all social media users, but, in particular, it is crucial for female users, as shown in the data. This is important for the designers and policymakers of the social media applications to understand and consider, as it would help them improve the current applications and create new ones.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.25-36
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• 2012

Social Commerce recently attracted the attention of academic and industry researchers. Social Commerce aims to make a transactional environment which is beneficial to three parties-social commerce service provider, buyer and seller by way of using the platform of SNS. As Social Commerce is a new technology issue, there is no existing conceptual framework, e.g. appropriate classification the business types, that help to understand the nature of Social Commerce. This study suggests one classification framework and tries to verify whether it works.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.37-46
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• 2022

The purpose of this study is to provide basic data onto preparing soft landing plan of future education policy by exploring direction of future education for the common good using big data and keyword network analysis. Based on the big data provided by Textom, data was collected under the keyword 'future education + common Good' and then keyword network analysis was performed. As a result of the research, it was found that 'common good', 'social', 'KAIST future warning', 'measures', 'research', 'future education', 'politics' were common keywords in the social awareness of future education for the common good. The results of this study suggest that the social awareness of future education for the common good is related to factors related to human, physical environment, social response, academic interest, education policy, education plan, and related variables, It was closely related. Based on these results, we suggested implications for the support for the preparation of a soft landing plan of future education for the common good.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.87-94
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• 2019

In this paper, the method of analyzing the unformatted data of consumers accumulated on social networks in the era of the Fourth Industrial Revolution by utilizing data from the service design and social psychology aspects was proposed. First, the fandom phenomenon, which shows subjective and collective behavior in a space on a social network rather than physical space, was defined from a data service perspective. The fandom model has been transformed into a collective level of customer Persona that has been analyzed at a personal level in traditional service design, and social network analysis that analyzes consumers' big data has been presented as an efficient way to pattern and visually analyze it. Consumer data collected through social leasing were pre-processed by column based on correlation, stability, missing, and ID-ness. Based on the above data, the company's brand strategy was divided into active and passive interventions and the effect of this strategic attitude on the growth direction of the consumer's fandom community was analyzed. To this end, the fandom model of consumers was proposed by dividing it into four strategies that the brand strategy had: stand-alone, decentralized, integrated and centralized, and the fandom shape of consumers was proposed as a growth model analysis technique that analyzes changes over time.

• Journal of Information Technology Applications and Management
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• v.23 no.2
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• pp.207-224
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• 2016

Investments in information and communication technologies (ICT) around the world have grown at an enormous rate over the past two decades, which reflects a new emphasis on consumer mobile devices. A social network service (SNS) is an online service that aims to build social relations among people who share interests and activities. The role of SNS is enormous for communicating ideas and opinions among social participants. The use of SNS has recently become one of the most popular social activities worldwide. This research investigated relation between personal characteristics, social characteristics and user satisfaction on SNS then, analyzed how these factors affecting continuance usage intention on SNS users. The conclusion is summarized as below. The study results show that informativeness, pleasure, innovativeness, relationship and empathy of SNS are having positive impact to some degree on the user satisfaction. Further, the user satisfaction of SNS users and quality of life have a positive impact on the continuance usage intention of SNS users. This results show that various SNS qualities are necessary to actively explore and obtain further information that users intend to find, while they are insufficient in function to provide the information other users require or exchange information with other users through the SNS.

• Korean Journal of Adult Nursing
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• v.20 no.5
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• pp.675-684
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• 2008

Purpose: The purpose of this study was to examine the relationship among cognitive function, depression, social support, and self-care in elderly with hypertension. Methods: The subjects were 132 elderly with hypertension living in Seoul, Korea. Data were collected through face-to-face interviews using the Korean version of Mini-Mental State Examination(MMSE-K), Short form geriatric depression scale, social support questionnaire 6, and hypertension self-care scale. Results: Thirty-four percent of the subjects had questionable dementia and forty-two percent of the subjects were depressed. Means for social support were 2.40 for network size and 4.07 for satisfaction. The mean score of hypertension self-care was 60.34, indicating that the subjects took care of themselves moderately well. Cognitive function was negatively related to depression. Social support network and satisfaction were negatively related to depression. Self-care was negatively related to social support network. Conclusion: Programs are needed for elderly with hypertension to improve their cognitive function, depression, and social support. Also further studies are needed to confirm the factors related to self-care in the elderly with hypertension.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.295-306
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• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.3
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• pp.872-880
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• 2016

The purpose of this study is to find the research trends relating to flipped learning through keyword network analysis. For investigating this topic, final 100 papers (removed due to overlap in all 205 papers) were selected as subjects from the result of research databases such as RISS, DBPIA, and KISS. After keyword extraction, coding, and data cleaning, we made a 2-mode network with final 202 keywords. In order to find out the research trends, frequency analysis, social network structural property analysis based on co-keyword network modeling, and social network centrality analysis were used. Followings were the results of the research: (a) Achievement, writing, blended learning, teaching and learning model, learner centered education, cooperative leaning, and learning motivation, and self-regulated learning were found to be the most common keywords except flipped learning. (b) Density was .088, and geodesic distance was 3.150 based on keyword network type 2. (c) Teaching and learning model, blended learning, and satisfaction were centrally located and closed related to other keywords. Satisfaction, teaching and learning model blended learning, motivation, writing, communication, and achievement were playing an intermediary role among other keywords.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.19-20
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• 2011

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.671-680
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• 2014

Recently, with the remarkable increase of social network services, it is necessary to extract interesting information from lots of data about various individual opinions and preferences on SNS(Social Network Service). The sentiment information can be applied to various fields of society such as politics, public opinions, economics, personal services and entertainments. To extract sentiment information, it is necessary to use processing techniques that store a large amount of SNS data, extract meaningful data from them, and search the sentiment information. This paper proposes an efficient method to extract sentiment information from various unstructured big data on social networks using HDFS(Hadoop Distributed File System) platform and MapReduce functions. In experiments, the proposed method collects and stacks data steadily as the number of data is increased. When the proposed functions are applied to sentiment analysis, the system keeps load balancing and the analysis results are very close to the results of manual work.