• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7607-7616
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• 2015

The present study sought to identify the differences in levels of depression and impulsivity depending on hours on SNS among Korean adolescents. Data for this study was collected from the responses of 1,008 of middle and high school students in Seoul in 2014. The upper 33%(N=364) and the lower 33%(N=319) of students who reported to spend lots of time on SNS were extracted from the total data to constitute two groups for analysis. The sample was analyzed utilizing SEM to compare depression and impulsivity levels of the upper group with those of the lower group while controlling for gender. In conclusion, there was a significant difference in level of depression and impulsivity between the two groups. The upper group that spent more time on SNS displayed higher levels of depression and impulsivity. The result of this study means that the more time the adolescents spend on SNS the more likely the adolescents are to be depressed and impulsive.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.428-435
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• 2016

Hundreds of millions of new posts and information are being uploaded and propagated everyday on Online Social Networks(OSN) like Twitter, Facebook, or Instagram. This paper proposes and implements a GPS-location based SNS data mapping, analysis, and visualization system, called Smart SNS Map, which collects SNS data from Twitter and Instagram using hundreds of PlanetLab nodes distributed across the globe. Like no other previous systems, our system uniquely supports a variety of functions, including GPS-location based mapping of collected tweets and Instagram photos, keyword-based tweet or photo searching, real-time heat-map visualization of tweets and instagram photos, sentiment analysis, word cloud visualization, etc. Overall, a system like this, admittedly still in a prototype phase though, is expected to serve a role as a sort of social weather station sooner or later, which will help people understand what are happening around the SNS users, systems, society, and how they feel about them, as well as how they change over time and/or space.

• Journal of Distribution Science
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• v.18 no.4
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• pp.87-96
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• 2020

Purpose: In this paper, we provide an illustration of Indonesian youth Social Networking Service (SNS) behavior and its relation to their culinary activity. Specifically, their behavior of culinary activity preferences and also the factors affecting their action of spending their money. Data and methodology: We gathered primary data from stratified random questionnaire survey (406 youth). The gathered data was analyzed using text data mining and statistics using R statistical computing language. Results: 1) We found out why our respondents are interested in following the accounts of SNS food influencers: i.e. visually attracted to the posts, as their reference to find places to dine out, as their reference to try new food menu and to get nostalgic feeling about the food. 2) The respondents decide to actually go to the recommended culinary places because of several factors, specifically, its description (visual and text), location, word of mouth (WoM), the experience of being to that place and price. 3) Important factors affecting culinary spent are income, number of following food influencer account, SNS usage time and their interest when looking at WoM. Conclusions: SNS behavior influences Indonesian youth culinary activity preferences and spent.

• Culinary science and hospitality research
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• v.22 no.8
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• pp.39-52
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• 2016

Present study was performed to identify the effects of SNS WOM characteristics on attitude and purchase intention as perceived in restaurant participants and to confirm the moderating effect according to SNS receivers' characteristics on formulated model. Survey method was employed to consumers who are using SNS to find restaurant foods through convenience sampling method in Seoul area. A total of 250 surveys were distributed and 230 were used for analysis, after excluding missing and unusual data. The results from this study are as follows. First, vividness of SNS WOM characteristics had a greater effect on attitude of restaurant food than timeliness, but the consensus and neutrality of SNS WOM characteristics have no significant effect on attitude of restaurant food. Second, SNS receivers characteristics found to moderate the relationships between vividness and timeliness of SNS WOM characteristics and attitude. However, SNS receivers characteristics did not have a moderating role relationships between consensus and neutrality of SNS WOM characteristics and attitude. Third, attitude of restaurant food had a significant effect on purchase intention.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.137-157
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• 2015

The development of Social Network Service (SNS) has brought many positive changes to the ways people communicate, interact and share information. However, using the SNS does not always leads to in a positive results, particularly when it is addictively used. In fact, the addictive use of SNS results in many negative effects in our society. Recently, SNS users feel negative emotions such as expecially stress and fatigue while using SNS. Thus, the purpose of this study is to empirically examine antecedents of user fatigue on SNS, which can be explained by the degree of Individual, environment and SNS characteristics. This study also examines consequences of user fatigue on SNS. Lastly, we examine the moderating effects of Habit among SNS fatigue, barrier of living and task performance decline. The data for empirical analysis were collected 401 responses on SNS users in Korea. The results of this study are as follows; First, reputation perception, loneliness, unwanted relation, privacy concern, information overload, social presence and interaction are significantly related to SNS fatigue. Second, SNS fatigue, barrier of living and Task performance decline are significantly related to discontinuous usage intention. Third, the moderating effect of Habit of SNS using is found in the relationship among SNS fatigue, barrier of living and task performance decline. Based on the results of this study, Theoretical and practical suggestions were discussed.

• Knowledge Management Research
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• v.16 no.4
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• pp.69-87
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• 2015

Given the huge number of data produced by its users, SNS is a great source of customer insights. Since viral trends in SNS reflect customers' direct feedback, companies can draw out highly meaningful business insights when such data is effectively analyzed and managed. However, while the importance of understanding SNS big data keeps growing, the methods for analyzing atypical data such as SNS postings for business insights over product has not been well studied. This study aims to demonstrate the way to exploit topic modeling method to support marketing strategy generation and therefore leverage business process. First, we conducted topic modeling analysis for twitter data of Apple and Samsung smartphones. Then we comparatively examined the analysis results to draw meaningful market insights about each smartphone product. Finally, we draw out a strategic marketing recommendation for each smartphone brand based on the findings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.139-150
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• 2020

The purpose of this study was to understand the influence of social network site (SNS) addiction on the ability of nursing students to adjust to university life and to generate the basic data to develop programs that could improve this ability. The data was collected from questionnaires that were filled out by 255 nursing students in two universities located in Jeollanam-do and Gyeonggi-do from May 16, 2020 to May 20, 2020. The data was analyzed using the SPSS 23.0 program (frequency, ANOVA, Pearson's correlation, multiple regression). The mean scores of SNS addiction and adjustment to university life were 2.16±0.54 (range:1-5) and 3.13±0.39 (range:1-5) respectively. SNS addiction accounts for 27% of the variance in adjustment to university life. The study concluded that SNS addiction negatively affects adjustment to university life among nursing students. To ensure better adjustment a program should be developed to treat SNS addiction early. Also, a study will have to be conducted to determine the time when tendency toward SNS addiction becomes apparent, to initiate treatment.

• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• Fashion & Textile Research Journal
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• v.18 no.1
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• pp.48-62
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• 2016

Consumers have come to demand high quality, affordable prices, and innovative product designs of the outdoor wear market due to their well-being and leisure oriented lifestyle. A new system of business in outdoor wear has emerged in the process through which corporations have endeavored to satisfy such consumer needs. Outdoor wear brands have utilized social network services (SNS) such as Facebook and Twitter as means of marketing and have built close relations with consumers based on communication through these media. Recently, explosively escalating SNS data are referred to as social big data, and now that every consumer online is a commentator, reviewer, and publisher, the outdoor wear market and all of its brands have to stop talking and start listening to how they are perceived. Therefore, this study employs Social $Metrics^{TM}$, a social big data analysis solution by Daumsoft, Inc., to verify changes in the allusions related to outdoor wear market found on SNS. This study aims to identify changes in consumer perceptions of outdoor wear based on changes in outdoor wear search words and trends in positive and negative public opinion found in SNS social big data. In addition, products of interest, the major brands mentioned, the attributes taken into consideration during purchases of products, and consumers' psychology were categorized and analyzed by means of keywords related to outdoor wear brands found on SNS. The results of this study will provide fundamental resources for outdoor wear brands' market entry and brand strategy implementation in the future.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.579-586
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• 2015

As smart-phone usage increases, the number of Social Networking Service (SNS) users has also exponentially increased. SNS allows people to efficiently exchange their personal opinion, and for this reason, it is possible to collect the reaction of each individual to a given event in real-time. Nevertheless, new methods need to be developed to collect and analyze people's opinion in real-time in order to effectively evaluate the impact of a TV advertisement. Hence, we designed and constructed a system that analyzes the effect of an advertisement in real-time by using data related to the advertisement collected from SNS, specifically, Twitter. In detail, Hadoop is used in the system to enable big-data analysis in parallel, and various analyses can be conducted by conducting separate numerical analyses of the degrees of mentioning, preference and reliability. The analysis can be accurate if the reliability is assessed using opinion mining technology. The proposed system is therefore proven to effectively handle and analyze data responses to divers TV advertisement.