• International journal of advanced smart convergence
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• 제12권1호
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• pp.164-172
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• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• 감성과학
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• 제20권3호
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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.

• 한국경영과학회지
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• 제37권2호
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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.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 가을 학술발표논문집 Vol.33 No.2 (D)
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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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• 제6권2호
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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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• 제22권4호
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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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• 제24권2호
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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.

• 디지털융복합연구
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• 제12권9호
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• pp.21-33
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• 2014

이 글에서는 한국 여성 결혼이주자정책의 특징과 한계를 상호문화주의(interculturalism)적 측면에서 조망하고 과제를 제시하고자 한다. 연구의 초점은 한국에서 압축적으로 급속하게 진행되고 있는 다인종사회화의 현황과 정책적 대응을 개괄하고, 여성 결혼이주자정책에서 나타나고 있는 인종 지향적이고 젠더 편향적인 이슈들을 검토한다. 그리고 여성 결혼이주자들은 '초국적 사회적 장'(transnational social field)에서의 독특한 정체성 재형성 과정을 거친다는 점에서 한국 여성 결혼이주자정책이 지향할 수 있는 현실적 지향점으로서의 상호문화주의의 개념과 특징, 유럽의 정책 및 사업 사례를 예시하고, '상호문화적 시민권'에 대한 인식 제고와 교육 프로그램 강화, '선택적 동화'를 통한 '다원적 통합' 지향, 여성 결혼이주자의 상호문화적응프로그램 강화, 한국의 특성을 고려한 상호문화성 측정 지표 개발과 평가 결과의 이주자정책에의 피드백 등 상호문화성 강화를 위한 과제를 제시한다.

• International Journal of Computer Science & Network Security
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• 제23권10호
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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.

• 커뮤니케이션디자인학연구
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• 제63권
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• pp.336-347
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• 2018

오늘날 국내 캠핑인구는 500만이 넘었고, 캠핑관련 산업도 지속적으로 성장하고 있지만 미국, 유럽과 일본 등 캠핑선진국에 비해 다소 미흡한 캠핑 문화와 수입 캠핑브랜드의 높은 의존도가 문제되고 있다. 따라서 국내산 캠핑 브랜드의 정착과 글로벌화 브랜드 육성을 위한 노력이 필요하다. 이에 본 연구에서는 국내산 캠핑 브랜드 정체성 수립과 차별화 전략을 위한 자료 제공을 위해 감성 인지적 접근을 통한 조사를 시행하였다. 감성적 접근을 위해 '캠핑, 캠프, 캠핑 브랜드, 캠핑 디자인'을 키워드로 빅 데이터 조사를 실시하고 분석하였으며, 인지적 접근을 통해 해외 유명 캠핑 브랜드 17개와 국내산 브랜드 10개에 대한 마켓조사를 실시하였다. 빅 데이터를 통한 조사 결과 소비자들의 정보 교류 경로를 유추할수 있었다. 캠핑 관련 연관어 및 마켓조사를 통해 브랜드 가치를 형성하는 주요 요소를 알수 있었다. 또한, 세계적인 유명 브랜드의 경우 각자의 브랜드 정체성이 뚜렷하며 독자적인 브랜드 스토리와 로고 디자인, 상품 디자인, 색채를 구축하고 있었으며 일관성을 지니고 있었다.