• Journal of Information Science Theory and Practice
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• v.8 no.3
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• pp.77-90
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• 2020

This study analyzes N=181 social media rumors from Bangladesh to find out the most popular themes, sources, and aims. The result shows that social media rumors have seven popular themes: political, health & education, crime & human rights, religious, religiopolitical, entertainment, and other. Also, online media and mainstream media are the two main sources of social media rumors, along with three tentative aims: positive, negative, and unknown. A few major findings of this research are: Political rumors dominate social media, but its percentage is decreasing, while religion-related rumors are increasing; most of the social media rumors are negative and emerge from online media, and social media itself is the dominant online source of social media rumors; and, most of the health-related rumors are negative and surge during a crisis period, such as the COVID-19 pandemic. This paper identifies some of its limitations with the data collection period, data source, and data analysis. Providing a few research directions, this study also elucidates the contributions of its results in academia and policymaking.

• Annals of Geriatric Medicine and Research
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• v.20 no.3
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• pp.149-159
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• 2016

Background: Since the mid-20th century, the ways in which social networks and older adults' health are related have been widely studied. However, few studies investigate the relationship between self-rated health and position in a complete social network of one entire Korean rural village. This study highlights use of a complete network in health studies. Methods: Using the Korean Social Life and Health Project, the population-based data of adults aged 60 or older and their spouses in one myeon in Ganghwa island (Ganghwa-gun, Incheon, Korea), Incheon, Korea (with a 95% response rate), this study built a $1,012{\times}1,012$ complete social network matrix of the village. The data were collected from 2011 to 2012, and 731 older adults were analyzed. The ordered logistic models to predict self-rated health allowed us to examine social factors from socio-demographic to individual community activities, ego-centered network characteristics, and positions in a complete network. Results: From the network data, 5 network components were identified. Even after controlling for all other factors, if a respondent belonged to a segregated component, the probability that he or she reported good health dropped substantially. Additionally, high in-degree centrality was connected to greater self-rated health. Conclusion: This finding highlights the importance of social position not only from the respondents' point of view but also from the entire village's perspective. Even if a respondent maintained a large social network, when all of those social ties belonged to a segregated group in the village, the respondent's health suffered from this segregation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2484-2498
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• 2017

Various types of social media make the people share their personal experience in different ways. In some social networking sites. Some users post their reviews, some users can support these reviews with comments, and some users just rate the reviews as kind of support or not. Unfortunately, there is rare explicit negative comments towards other reviews. This means if there is a link between two users, it must be positive link. Apparently, the negative link is invisible in these social network. Or in other word, the negative links are redundant to positive links. In this work, we first discuss the feature extraction from social media data and propose new method to compute the distance between each pair of comments or reviews on social media. Then we investigate whether we can predict negative links via regression analysis when only positive links are manifested from social media data. In particular, we provide a principled way to mathematically incorporate multi-facet data in a novel framework, Constructing Negative Links, CsNL to predict negative links for discovering the hidden information. Additionally, we investigate the ways of solution to general negative link predication problems with CsNL and its extension. Experiments are performed on real-world data and results show that negative links is predictable with multi-facet of social media data by the proposed framework CsNL. Essentially, high prediction accuracy suggests that negative links are redundant to positive links. Further experiments are performed to evaluate coefficients on different kernels. The results show that user generated content dominates the prediction performance of CsNL.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.4
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• pp.169-181
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• 2018

Under the constantly intensifying global competition environment, many companies are exploring new business opportunities in the field of social innovation using creating shared value. In seeking social innovation, it is a key starting point of social innovation to clarify the problem to be solved and to grasp the cause of the problem. Among the many problem solving methodologies, design thinking is getting the most attention recently in various fields. Design Thinking is a creative problem solving method which is used as a business innovation tool to empathize with human needs and find out the potential desires that the public does not know, and is actively used as a tool for social innovation to solve social problems. However, one of the difficulties experienced by many of the design thinking project participants is that it is difficult to analyze the observed data efficiently. When analyzing data only offline, it takes a long time to analyze a large amount of data, and it has a limit in processing unstructured data. This makes it difficult to find fundamental problems from the data collected through observation while performing design thinking. The purpose of this study is to integrate qualitative data analysis and quantitative data analysis methods in order to make the data analysis collected at the observation stage of the design thinking project for social innovation more scientific to complement the limit of the design thinking process. The integrated methodology presented in this study is expected to contribute to innovation performance through design thinking by providing practical guidelines and implications for design thinking implementers as a valuable tool for social innovation.

• Asia pacific journal of information systems
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• v.30 no.3
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• pp.568-589
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• 2020

Recently, Social media has become a concern for businesses, including Small and Medium Enterprises (SMEs). SMEs began to adopt social media to support their performance. To benefit from the application of social media, SMEs must implement the right strategy. This study aims to analyze the factors that influence the use of social media in SMEs. Furthermore, alignment between social media functionalities and strategies and their effect on SME's performance are investigated. A case study is conducted in Gymi, a garment SMEs in East Java, Indonesia. The data collection includes interviews with the owner of SMEs, observations, and document analysis. Data analysis is performed by pattern matching, which matches the patterns from the literature with data from the case study. The results of this study show that cost-effectiveness, interactivity, and compatibility are factors that influence the use of social media in Gymi. The social media used by Gymi are Instagram, Facebook, YouTube, WhatsApp, and LINE. However, the main social media used to support Gymi's functions is Instagram. Gymi has a relatively good social media strategy as it has defined a specific goal, target audience, and channel selection for social media (Instagram). It also has specific resources and policies to handle social media. Gymi monitors and evaluates their social media content activities. These strategies are aligned with the Instagram feature used to support Gymi's function, particularly marketing, sales, customer service, and to some extent, internal operation. The alignment contributes to Gymi's performance measured by the increase in reputation (number of Instagram followers) and sales.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2439-2446
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• 2013

Recently, SNS (Social Network Service) such as Twitter and Facebook has grown dramatically because of smart phones. Since development of IT has created massive information, social big data extremely increased. Competition between corporations is getting more intense, so they need customer feedback in order to fulfill an effective management. Because social big data plays an important role for getting customer feedback, a lot of corporations are interested in analyzing and applying of social big data. Collecting and analyzing social big data is operated by Buzz monitoring system. This paper demonstrates the research of buzz monitoring system that analyzes big data, and presents examples of customer reputation using buzz monitoring. In the paper, after all, it would analyze the result from the customer reputation, and research the implication.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.2
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• pp.157-171
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• 2014

A organization of social enterprises is to achieve various goals such as private interests, the public nature, and social policy. For fulfilling these goals, we have to understand the various success factors. These success factors were shared among peoples. This study explored a position of structure of social network formed by success factors of Social Enterprises with importance. A position within social network defined a number of link connected other nodes. A position is closely associated with to individual's behaviors, opinions and thinking. We used social network analysis with two mode method for explaining feathers of structure of social network formed by success factors shared among peoples. We choose degree centrality for determining a position within social network. Centrality is a key measure in social network analysis. Results is that shared success factors are operation capital(15.15%) totally, and by Buying experience of products of Social Enterprises, Business Compliance(14.39%) and planning(12.88%), and by usage time of smart devices, Business Support(17.05%) and planning(16.10%). and the dominant success factor was not explored.

• The Journal of Industrial Distribution & Business
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• v.9 no.11
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• pp.25-38
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• 2018

Purpose - This study aimed to identify the sub-dimensions of fashion social media quality (information quality, social quality, service quality, system quality) and investigate how they affect purchase intention through fashion information use behavior (information acceptance, information diffusion). Research design, data, and methodology - Data collection was carried out twice for systematic verification of the research model. In the first data collection, the reliability and validity of research variables were verified through 238 respondents and questionnaires were revised and supplemented based on their responses. In March 2018, the final survey was conducted from 755 respondents the age of 20 to 49. Using SPSS 23.0, descriptive statistics, exploratory factor analysis, correlation analysis were performed. In order to test hypotheses, structural equational modeling technique was employed using AMOS 23.0. Results - First of all, fashion Social media quality consists of four factors including information quality, social quality, service quality and system quality. Second, fashion Social media information quality, social quality, and system quality were shown to have a positive(+) effect on information acceptance behavior, and social quality, service quality and system quality were shown to have a positive(+) effect on information diffusion behavior. It was also determined that the acceptance and diffusion behaviors of fashion information through fashion Social media had positive(+) influence on purchase intention. Conclusions - This study holds academic significance in its identification of the components of fashion Social media quality and for conducting an empirical analysis on the causal relationship between fashion information acceptance and diffusion behaviors, and purchase intention. The results of this study indicate that fashion involvement is the key factors in determining the quality of Social media, the acceptance of information through Social media, and, by extension, the purchase of fashion products. Practitioners in the fashion industry may use the findings of this study in order to build more effective Social media strategy.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.173-183
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• 2019

This study aims to utilize big data contents of news and social media for developing a corporate strategy of multinational enterprises and their global decision-making through the data mining technique, especially text mining. In this paper, the data of 2 news media (BBC and CNN) and 2 social media (Facebook and Twitter) were collected for the three global leading Korean companies (Samsung, Hyundai Motor Company, and LG) from April, 2018 to April, 2019. The findings of this paper have shown that traditional news media and also modern social media have become devastating tools to extract global trends or phenomena for businesses. Moreover, this presents that a company can adopt a two-track strategy through two different types of media by deriving the key issues or trends from news media channels and also grasping consumers' sentiments, preference or issues of interest such as battery or design from social media. In addition, analyzing the texts of those media and understanding the association rules greatly contribute to the comparison between two different types of media channels to see the difference. Lastly, this provides meaningful and valuable data empowered insights to find a future direction comprehensively and develop a global strategy for sustainability of business.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.258-267
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• 2020

This study identified the relationship between professional competence of social workers, organizational commitment, and happiness, and then analyzed the effect of professional competence on the happiness of social workers and the mediating effect of organizational commitment. The survey for data collection was conducted among employees of social welfare facilities located in Gyeonggi Province. We analyzed the data from 203 participants who agreed to the purpose of this study. For the collected data, descriptive statistics, correlation, and multiple regression analyses were performed using SPSS 22.0. The data analyses showed that the professional competence of social workers was found to have a meaningful effect on the happiness and organizational commitment of social workers. Second, organizational commitment of social workers was found to have a meaningful effect on happiness. Third, organizational commitment was found to have a partial mediating effect on the relationship between professional competence and the happiness of social workers. Based on the results, this study suggested that professional competence affects the happiness of social workers and also recommends practical plans that can improve organizational commitment.