• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.23-38
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• 2017

In order to identify effective approaches for creating more viral Facebook posts, this research conducted an empirical content analysis of leading Korean brands' Facebook fan-pages (Samsung Mobile, SK Telecom, Kia Motors, and POSCO). Their distinctive visual storytelling and communication patterns were investigated as effective user engagement triggers. Through analysis of the research results, it was statistically proved that the different industrial attributes of the four brands, which are primarily characterized by their product (or service) types, affect their Facebook posting patterns by showing different engaging rates (measured by like, comment, and share metrics). In addition, the user engagement rates of the posts were influenced by their visual storytelling factors (i.e. ad objective, value scale, and visual media types). In line with these statistical findings, the distinctive visual storytelling strategies of the four brands were identified. Moreover, competitive and uncompetitive visual storytelling tactics were suggested according to the ad objectives and visual media types on Facebook.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.2
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• pp.1-16
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• 2012

NCW(Network Centric Warfare) offers BSEs(Battelspace-Entities) the capabilities of sharing information through C4ISRCommand, Control, Communications, Computers and Intelligence, Surveillance and Reconnaissance) network and it also improves their combat powers based on information superiority with awareness of common situation in battlefield and self-synchronization. Although the concept of NCW was developed at the end of 1990 and there have been various studies for NCW from the development of its concept, the effort for measuring the synergistic effect of NCW is insufficient at the present time. Therefore, in this paper we reviewed literatures concerning the effectiveness measurement of NCW. The category of our survey is network effect, metrics, simulation, battlefield information, social network analysis and mathematical model. The main purpose of this study is to suggest future researchers a research direction by analyzing the aspects and limitations of existing studies about the quantitative measurement of NCW.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.135-139
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• 2006

Today's social networking services have tens of millions of users, and are growing fast. Their sheer size poses a significant challenge in capturing and analyzing their topological characteristics. Snowball sampling is a popular method to crawl and sample network topologies, but requires a high sampling ratio for accurate estimation of certain metrics. In this work, we evaluate how close topological characteristics of snowball sampled networks are to the complete network. Instead of using a synthetically generated topology, we use the complete topology of Cyworld ilchon network. The goal of this work is to determine sampling ratios for accurate estimation of key topological characteristics, such as the degree distribution, the degree correlation, the assortativity, and the clustering coefficient.

• International Journal of Knowledge Content Development & Technology
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• v.6 no.2
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• pp.67-92
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• 2016

Employing a newly developed conceptual framework of the tasks and activities that comprise today's digital scholarly undertaking and their potentially reputation building, maintaining and enhancing components, the efforts of ResearchGate in supporting scholars' reputation building endeavours were put under the microscope. Not unexpectedly, RG performs well in regard to basic research activities. Clearly, too, with ten metrics at its disposal, RG is in a league of its own when it comes to monitoring individual research reputation. Where RG falls down is regarding scholarly activities that do not concern pure research and so especially teaching. Its claim to have created a new way of measuring reputation is only partially true because if it wants to do so genuinely then it needs to extend the range of scholarly activities covered. RG also falls short in informing members as to the nature and changes to its service and of embracing new actors, such as citizen scientists and amateur experts.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.131-138
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• 2022

A Mobile Ad-hoc Network (MANET) is a collection of moving nodes that communicate and collaborate without relying on a pre-existing infrastructure. In this type of network, nodes can freely move in any direction. Routing in this sort of network has always been problematic because of the mobility of nodes. Most existing protocols use simple routing algorithms and criteria, while another important criterion is path selection. The existing protocols should be optimized to resolve these deficiencies. 'Particle Swarm Optimization (PSO)' is an influenced method as it resembles the social behavior of a flock of birds. Genetic algorithms (GA) are search algorithms that use natural selection and genetic principles. This paper applies these optimization models to the OLSR routing protocol and compares their performances across different metrics and varying node sizes. The experimental analysis shows that the Genetic Algorithm is better compared to PSO. The comparison was carried out with the help of the simulation tool NS2, NAM (Network Animator), and xgraph, which was used to create the graphs from the trace files.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of Digital Convergence
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• v.12 no.9
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• pp.21-33
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• 2014

This is a research on the characteristic and its limit of Korean migrant women policy to prospect and suggest in the point of interculturalism. The focus of this paper is in summing-up to current situation of multiethnic society which rapidly progressing in Korea and in reviewing the race-oriented, gender-biased issue in the migrant women policy in Korea. However, the migrant women go through by the unique rebuilt progress in the transnational social field which can be continue for several or for decades between delivery country and inflow country but the one-sided, certain movement to a new country. In the above mentioned standpoint, this paper can suggest the implication for the concept and its character of interculturalism, the policy and undertasking case in Europe as a realistic directing point on which the migrant women policy in Korea. The educational program consolidation of intercultural citizenship, the orientation of pluralistic integration through selective assimilation, the consolidation of intercultural adaptation program, the intercultural measurement metrics development and feedback which considered of Korean characteristics are proposed in this paper.

• 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.

• Journal of Communication Design
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• v.63
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• pp.336-347
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• 2018

Nowadays, in Korea, the number of campers is increased over 5 million. Many Korean camping brands have excellent qualities however, a lot of times weak brand identities to be globally known. The purpose of this study is to provide helpful sources to have strong brand identities, add more values based on related word research from big data and market research. The data is to be analysed by sensible & cognitive approaches. The keywords for the sensible research are 'camping, camp, camping brand, and camping design'. Then 17 representative oversea brands and 10 Korean brands were analysed for the market researches. From related word research from big data, we can find out the thinking process of potential consumers, how people communicates to exchange information, and what can be the sources to add brand values. Also from the market researches, we were able to find that successful brands have distinctive brand identities, stories, logos with representable colors and they continuous produce signature designs and own way of color matching.

• Journal of Smart Tourism
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• v.4 no.2
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• pp.5-14
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• 2024

This study explores the transformative potential of machine learning (ML) and ML-driven data analytics in the hospitality industry. It provides a comprehensive overview of this emerging method, from explaining ML's origins to introducing the evolution of ML-driven data analytics in the hospitality industry. The present study emphasizes the shift embodied in ML, moving from explicit programming towards a self-learning, adaptive approach refined over time through big data. Meanwhile, social media analytics has progressed from simplistic metrics deriving nuanced qualitative insights into consumer behavior as an industry-specific example. Additionally, this study explores innovative applications of these innovative technologies in the hospitality sector, whether in demand forecasting, personalized marketing, predictive maintenance, etc. The study also emphasizes the integration of ML and social media analytics, discussing the implications like enhanced customer personalization, real-time decision-making capabilities, optimized marketing campaigns, and improved fraud detection. In conclusion, ML-driven hospitality data analytics have become indispensable in the strategic and operation machinery of contemporary hospitality businesses. It projects these technologies' continued significance in propelling data-centric advancements across the industry.