• Fashion & Textile Research Journal
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• v.23 no.1
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• pp.31-43
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• 2021

This study analyzes the characteristics of fashion flex, which have recently spread on social media. The study was conducted with big data analysis that derived flex keywords from news articles and social media as well as case studies that collected 136 posted images on Instagram to analyze the content. The meaning of flex was positively accepted based on big data results. Flex was also a buzzword frequently used on social media as well as a symbolic meaning when discussing luxury goods or fashion brand experiences. The characteristics of fashion flex in social media were largely divided into three categories. First, conspicuous consumption is considered an active expression of individual fashion tastes or self-oriented consumption and emphasizes individuality through consumption. The second characteristic is that the public actively participates in events or fashion flex challenges. People use similar fashion styles or products to participate in playful social interactions with others using various Instagram functions. Finally, acts of pursuing psychological well-being in social media were used as the term flex in a broad sense and were shown to actively explore fashion-related materials and experiences for individual happiness. This study found that the meaning of existing conspicuous consumption is transforming into positive consumption, such as the expression of taste-based identity or the seeking of fun and psychological well-being. It is also meaningful that fashion has become an effective means to express individuality and taste in expressing flex.

• The Journal of Information Systems
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• v.26 no.2
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• pp.1-23
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• 2017

Purpose Facebook is a social network service that has the highest number of Monthly Active Users around the world. Hence, marketers have selected Facebook as the most important platform to get customer engagement. With respect to the customer engagement enhancement, the most popular and engaging post type in the Facebook brand fan pages related to what was usually classified as 'sweepstakes'. Sweepstakes refer to a form of gambling where the entire prize may be awarded to the winner. Which makes customers more engaged with the brand. This study aims to explore sweepstakes-oriented social media marketing approaches based on the application of big data analytics. Design/methodology/approach we collect sweepstakes data from each company based on the data crawling from the Facebook brand fan pages. The output of this study explains how companies in each category of FCB grid can design and apply sweepstakes for their social media marketing. Findings The results show that they have one thing in common across the four quadrants of FCB grid. Regardless of the quadrants, most frequently observed type is 'Simple/Quiz or Comments/Quatrains [event type of sweepstakes] + Gifticon [type of reward prize] + Image [type of message display] + No URL [Link toother website] +Single-Gift-Offer [type of reward prize payment]'. So, if the position of the brand is hard to be defined by the FCB grid model, then this general rule can be applied to all types of brands. Also some differences between the quadrants of the FCB grid were observed. This study offers several research implications by analyzing Sweepstakes-oriented social media marketing approaches in Facebook brand fan pages. By using the FCB grid model, this study provides guidance on how companies can design their sweepstakes-oriented social media marketing approaches in the context of Facebook brand fan pages by considering their context.

• 한국체육학회지인문사회과학편
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• v.56 no.4
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• pp.397-413
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• 2017

The purpose of this study is to introduce the case and to provide related information for the physical education major to handle and utilize the social big data through the exploratory study for the application of sports industry in the fourth industrial revolution. For this study, data was collected from the article database, which covers the keyword such as 'Social Big Data', 'Sports' and so on. The analyzed articles were 86 articles. As a results, The research on social big data applied to sports industry are as follows: 1) Analysis of major issues related to sports fans' interests and sports events, 2) A study on media sports engagement, 3) The prediction analysis of sports game based on the sentiment analysis, 4) Development of salary estimation model for professional player in sports, 5) Research trend analysis and so on. In conclusion, the social big data analysis technology in the sports industry and management can be utilized variously. Therefore, the specialists of the sports industry and management field need to learn the techniques, to acquire the know-how for the research project, to convert the convergence thinking.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.93-94
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• 2015

본 논문에서는 학교 교육환경에서 청소년들에게 발생 할 수 있는소 셜미디어의 역기능을 빅 데이터 처리를 통하여 분석 할 수 있는 방법을 제시하고, 특히 악성 댓글을 위주로 한 청소년들 간의 소셜미디어를 중심으로 빅 데이터의 마이닝 기술을 활용하여 대표적인 청소년 문제의 확산을 방지 할 수 있는 시스템 제안한다.

• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.6
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• pp.1034-1044
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• 2016

This study seeks a method to objectively evaluate sensibility based on Big Data in the field of design. In order to do so, this study examined the sensibility responses on design factors for the public through a network analysis of texts displayed in social media. 'Hanbok', a formal clothing that represents Korea, was selected as the subject for the research methodology. We then collected 47,677 keywords related to Hanbok from 12,000 posts on Naver blogs from January $1^{st}$ to December $31^{st}$ 2015 and that analyzed using social matrix (a Big Data analysis software) rather than using previous survey methods. We also derived 56 key-words related to design elements and sensibility responses of Hanbok. Centrality analysis and CONCOR analysis were conducted using Ucinet6. The visualization of the network text analysis allowed the categorization of the main design factors of Hanbok with evaluation terms that mean positive, negative, and neutral sensibility responses. We also derived key evaluation factors for Hanbok as fitting, rationality, trend, and uniqueness. The evaluation terms extracted based on natural language processing technologies of atypical data have validity as a scale for evaluation and are expected to be suitable for utilization in an index for sensibility evaluation that supplements the limits of previous surveys and statistical analysis methods. The network text analysis method used in this study provides new guidelines for the use of Big Data involving sensibility evaluation methods in the field of design.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.112-118
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• 2024

We Design The problem of underdeveloped areas is emerging as a social problem. Industrialization drove the population to the cities, creating underdeveloped areas. Underdeveloped areas are causing social problems such as population decline and aging. It is necessary to study the continuous tourism development of underdeveloped areas through development and improvement projects. Using social media big data to investigate keywords in underdeveloped areas and see the connection between keywords. The purpose of this study was to conduct core research divided by type and to investigate the keywords of tourism in underdeveloped areas through concor analysis of underdeveloped areas. As a result of the study, keywords were connected for each type of redevelopment, regional development, regional economy, and underdeveloped areas. Through this, the keywords for sustainable tourism in underdeveloped areas were identified. It is hoped that this study will develop sustainable tourism for the keywords of underdeveloped areas.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.51-58
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• 2016

In the area of hotel and tourism sector, various research are analyzed using big data. Big data is being generated by any digital devices around us all the times. All the digital process and social media exchange produces the big data. In this paper, we analyzed the de-identification method of big data to use the personal information of hotel guests. Through the analysis of these big data, hotel can provide differentiated and diverse services to hotel guests and can improve the service and support the marketing of hotels. If the hotel wants to use the information of the guest, the private data should be de-identified. There are several de-identification methods of personal information such as pseudonymisation, aggregation, data reduction, data suppression and data masking. Using the comparison of these methods, the pseudonymisation is discriminated to the suitable methods for the analysis of information for the hotel guest. Also, among the pseudonymisation methods, the t-closeness was analyzed to the secure and efficient method for the de-identification of personal information in hotel.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.261-266
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• 2021

The spread of cloud computing, digital computing, and the popular social media platforms have led to increased growth of data. That growth of data results in what is known as big data (BD), which seen as one of the most strategic resources. The analysis of these BD has allowed generating value from massive raw data that helps in making effective decisions and providing quality of service. With Vision 2030, Saudi Arabia seeks to invest in BD technologies, but many challenges and barriers have led to delays in adopting BD. This research paper aims to search in the state of Big Data Analytics (BDA) in Saudi higher education sector, identify the barriers by reviewing the literature, and then to apply the design-reality gap model to assess these barriers that prevent effective use of big data and highlights priority areas for action to accelerate the application of BD to comply with Vision 2030.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.780-786
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• 2015

Social media, such as Social Network Service include a lot of spontaneous opinions from customers, so recent companies collect and analyze information about customer feedback by using the system that analyzes Big Data on social media in order to efficiently operate businesses. However, it is difficult to analyze data collected from online sites accurately with existing morpheme analyzer because those data have spacing errors and spelling errors. In addition, many online sentences are short and do not include enough meanings which will be selected, so established meaning selection methods, such as mutual information, chi-square statistic are not able to practice Emotional Classification. In order to solve such problems, this paper suggests a module that can revise the meanings by using initial consonants/vowels and phase pattern dictionary and meaning selection method that uses priority of word class in a sentence. On the basis of word class extracted by morpheme analyzer, these new mechanisms would separate and analyze predicate and substantive, establish properties Database which is subordinate to relevant word class, and extract positive/negative emotions by using accumulated properties Database.