• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.135-140
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• 2012

Massive numbers of users of social networks share various types of information such as opinions, news, and ideas in real time. As a new form of social network, Twitter is a particularly useful information source. Studying influence can help us better understand the role of social networks. The popularity of social networks like Twitter is primarily measured by the number of followers. The number of followers in Twitter and the number of users exposed to news media are important factors in measuring influence. We chose Twitter and the New York Times as representative media to analyze the influence and present an empirical analysis of these datasets. When the correlation between the number of followers in Twitter and the number of users exposed to the New York Times is computed, the result is moderately high. The correlation between the number of users exposed to the New York Times and the number of sections including the users on it, was found to be very high. We measure the normalized influence score using our proposed expression based on the two correlation coefficients.

• 인지과학
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• 제26권4호
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• pp.435-452
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• 2015

최근 개체 링킹에 대한 연구들은 지식 베이스를 외부 자원으로 사용하여 실세계의 지식과 의미적인 관련도를 통해 중의성을 해소하는데 중점을 두고 있다. 지식 베이스를 사용한 개체 링킹은 신문기사나 블로그 포스트 등에서는 좋은 성능을 보이지만, 마이크로블로그에서는 짧은 텍스트 길이와 지식 베이스에 존재하지 않는 주제를 다루는 특성 때문에 비교적 낮은 성능을 보인다. 본 논문에서는 140자가 되지 않는 짧은 텍스트 내에서 실시간으로 빠르게 정보를 공유하는 특성을 가지는 마이크로블로그에서 나타나는 개체명의 중의성을 해소하는 방법을 제안한다. 제안하는 방법은 지식 베이스만 사용하는 개체 링킹의 한계를 극복하기 위해 마이크로블로그 사용자 기록과 뉴스 기사를 이용하고, 지식 베이스에 존재하는 특정 엔트리로 개체 링킹을 수행한다. 본 논문에서는 개체명을 포함하는 한국어 트윗을 추출하여 데이터를 구축하였다. 성능 평가는 정확도 지표(시스템이 정답으로 판정한 데이터 개수/전체 데이터 개수)를 사용하였으며, 제안하는 시스템은 구축한 데이터에서 기존 지식 베이스만 사용한 개체 링킹 시스템보다 높은 67.7%의 정확도를 나타내었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.835-838
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• 2000

Popular methods for extracting a text region in video images are in general based on analysis of a whole image such as merge and split method, and comparison of two frames. Thus, they take long computing time due to the use of a whole image. Therefore, this paper suggests the faster method of extracting a text region without processing a whole image. The proposed method uses line sampling methods, FFT and neural networks in order to extract texts in real time. In general, text areas are found in the higher frequency domain, thus, can be characterized using FFT The candidate text areas can be thus found by applying the higher frequency characteristics to neural network. Therefore, the final text area is extracted by verifying the candidate areas. Experimental results show a perfect candidate extraction rate and about 92% text extraction rate. The strength of the proposed algorithm is its simplicity, real-time processing by not processing the entire image, and fast skipping of the images that do not contain a text.

• 한국융합학회논문지
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• 제12권7호
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• pp.31-36
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• 2021

중국 우한발 코로나 19 바이러스로 인하여 세계 경제가 침체하여, 미국연방준비제도를 비롯한 대부분 국가에서는 통화량을 늘려 경기를 부양하는 정책을 내놓았다. 주식 투자자들 대부분은 기업에 대한 재무제표 분석이 없이 유명 유튜버의 추천종목이나 지인의 말만 듣고 투자하는 경향이 있어서 주식투자의 손실 가능성이 크다. 따라서, 본 연구에서는 기존 자동매매 조건에서 발전된 인공지능 딥러닝 기법을 이용하여 주가에 영향을 미치는 거시지표를 분석하고 예측하여 주가에 미치는 상관관계를 통한 개별주가예측에 가중치를 부여하고 주가를 예측한다. 또한, 주가는 실시간 증시뉴스에 민감하게 반응하기 때문에 증시뉴스 텍스트 마이닝을 통하여 인공지능으로 예측된 주가에 가중치를 반영하여 더 정확한 주가 예측을 하여 주식 투자자에게 매매의 판단 근거를 제공하여 건전한 주식투자가 되도록 이바지하였다.

• 한국IT서비스학회지
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• 제14권2호
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• pp.159-176
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• 2015

As a public medium and one of the big data sources that is accumulated informally and real time, online news comments or replies are considered a significant resource to understand mentalities of article readers. The comments are also being regarded as an important medium of WOM (Word of Mouse) about products, services or the enterprises. If the diffusing effect of the comments is referred to as the degrees of agreement and disagreement from an angle of WOM, figuring out which characteristics of the comments would influence the agreements or the disagreements to the comments in very early stage would be very worthwhile to establish a comment-based eWOM (electronic WOM) strategy. However, investigating the effects of the characteristics of the comments on eWOM effect has been rarely studied. According to this angle, this study aims to conduct an empirical analysis which understands the characteristics of comments that affect the numbers of agreement and disagreement, as eWOM performance, to particular news articles which address a specific product, service or enterprise per se. While extant literature has focused on the quantitative attributes of the comments which are collected by manually, this paper used text mining techniques to acquire the qualitative attributes of the comments in an automatic and cost effective manner.

• 정보관리연구
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• 제29권2호
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• pp.1-17
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• 1998

분산되고 복잡한 정부 및 공공기관의 현행 기업지원체제의 어려움을 해소하기 위하여 창업, 자금, 기술, 입지, 판로, 무역투자 등 기업의 모든 경영활동에 관련된 정보를 체계적이고 종합적으로 구축, 제공하고 기업의 애로 및 민원을 신속히 발굴, 해소하기 위하여 정부 및 관련기관을 인터넷을 통해 상호 연계하는 종합기업서비스정보망의 구축과 이를 효율적으로 운영하기 위한 추진체계에 대하여 기술하였다.

• Journal of Multimedia Information System
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• 제8권4호
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• pp.285-294
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• 2021

The development process of domestic CG (Computer Graphics) needs to be studied by classifying the technical part and the design part. This paper analyzes how early domestic broadcast equipment evolved and focused on what went through the development process. Domestic broadcasting companies have introduced and used overseas equipment and have begun to develop their own technology. The development process of the early domestic production technology of broadcasting stations was classified into Character Generator, On-line Real-Time Graphic and Sport Coder. It was found that the orientation of broadcasting technology was conclusively focused on visualization, with socio-cultural factors acting along with the evolution of hardware and software. This research is meaningful in reorganizing the history of the development process of domestic CG technology in the early days through previous research (primary verification), newspaper articles and news coverage (secondary verification). This paper looks at what is missing by regaining the past of CG, and argues that with the advent of new technologies today, we must develop through appropriate division of roles and collaboration between engineers and designers.

• 한국IT정책경영학회 논문지
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• 제11권6호
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• pp.1503-1507
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• 2019

온라인 연예 뉴스 기사의 연성화와 속보성 기사가 증가함에 따라 많은 사람들이 연예면 기사를 접하며, 연예인에 대한 평가를 내릴 수 있게 됐다. 연예인에 대한 평판은 소속된 연예인 자원을 최대한 활용해야 하는 연예기획사의 사업전략에 핵심적인 요소이나, 실시간적으로 대규모 기사가 올라오는 환경에서 어떤 뉴스 기사가 어떤 연예인에 관한 것인지 체계적으로 분석하는 것은 용이하지 않다. 본 논문은 연예 뉴스 데이터에서 언급되는 연예인의 언급량을 기준으로 해당 기사의 주제가 되는 연예인을 추출하고, 해당 연예인의 연예기획사로 연관짓는 연예 뉴스 키워드 분석 시스템을 제안한다. 본 논문에서 제안된 시스템을 통해 광고사 혹은 연예기획사 측에서 사업을 위한 참고 자료로 해당 연예인의 가치 판단을 할 수 있다. 이와 더불어 증권사나 투자자들에게 연예기획사의 전망을 예측하여, 투자 전략의 토대를 마련해줄 수 있다.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Asia pacific journal of information systems
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• 제24권2호
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.