• Journal of Contemporary Eastern Asia
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• 제18권2호
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• pp.33-48
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• 2019

The current Moon Jai-in administration in South Korea is facing serious challenges as a result of a scandal involving the manipulation of news online. Staff in Moon's camp are suspected of manipulating public opinion by creating millions of fake news comments online, contributing to Moon being elected president. This South Korean political scandal raises a number of theoretical issues with regard to new platform technologies and media manipulation. First, the incident exposes the technological limits of blocking manipulation of the news, partly because of the nature of social media and partly because of the nature of contemporary technology. Contemporary social media is often monopolistic in nature; with the majority of people are using the same platforms, and hence it is likely that they will be subject to forms of media manipulation. Second, the Korean case of news manipulation demonstrates a unique cultural aspect of Korean society. News comments and readers' replies have become a major channel of alternative news in Korea. This phenomenon is often designated as "reply journalism," since people are interested in reading the news replies of ordinary readers equally to reading news reports themselves. News replies are considered indicators of public opinion and are seen as affecting trias politica in Korean society. Third, the Korean incident of news manipulation implicates a new form of populism in the 21st century and the nature of democratic participation. This article aims to explicate key issues in media manipulation by including wider technological, cultural, and political aspects in the South Korean news media context.

• 지식경영연구
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• 제14권4호
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• pp.1-17
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• 2013

As online communities proliferate, online news sites have received great attention in news media research. Although most of the online news sites provide contents for free, some have adopted the Pay-What-You-Want (PWYW) model by offering a voluntary payment option to the readers. In this study, we investigate the factors which influence subscribers' voluntary payment behavior on an online news site. Drawing upon both the Stimulus-Organism-Response (SOR) framework and the Elaboration Likelihood Model (ELM), we hypothesize that appreciation has a direct effect on the subscribers' voluntary payment behavior, whereas central factors (positive emotional content, cognitive content) and peripheral factors (news sharing, news article length) of the news articles have indirect impacts on voluntary payment behavior through the enhanced appreciation. Based on an empirical analysis of 172 news articles from the Korean online news site that adopted the PWYW pricing model (i.e., Ohmynews.com), we find that appreciation plays a critical role in voluntary payment behavior and that peripheral factors have significant impacts on appreciation. However, the impacts of central factors on appreciation are not found. By identifying influencing factors of subscribers' voluntary payment behavior on online news sites for the first time, this paper suggests a prospective alternative profit model for online news providers faced with fierce competition.

• Asian Journal for Public Opinion Research
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• 제9권1호
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• pp.48-82
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• 2021

This research article attempts to understand the current situation of fake news on social media in India. The study focused on four characteristics of fake news based on four research questions: subject matter, presentation elements of fake news, debunking tool(s) or technique(s) used, and the social media site on which the fake news story was shared. A systematic sampling method was used to select a sample of 90 debunked fake news stories from two Indian fact-checking websites, Alt News and Factly, from December 2019 to February 2020. A content analysis of the four characteristics of fake news stories was carefully analyzed, classified, coded, and presented. The results show that most of the fake news stories were related to politics in India. The majority of the fake news was shared via a video with text in which narrative was changed to mislead users. For the largest number of debunked fake news stories, information from official or primary sources, such as reports, data, statements, announcements, or updates were used to debunk false claims.

• 한국산학기술학회논문지
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• 제19권10호
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• pp.176-182
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• 2018

본 연구는 건설 안전사고에 대한 트랜드 분석을 위해 LDA(Latent Dirichlet Allocation) 기반의 토픽모델링(Topic Modeling)을 제시하여 분석하고자 한다. 특히, 건설산업의 안전사고를 예방하기 위해 제시되고 있는 기존의 다양한 정형데이터 분석에서 벗어난 비정형 데이터 분석 기반의 토픽 모델링을 통해 건설 안전사고 주요 핵심 키워드의 흐름에 대해 파악이 가능하다. 본 방법론을 적용하기 위해 540개의 건설 안전사고 관련 뉴스데이터를 수집하였다. 이를 기반으로, 10가지 토픽과 각 토픽 내의 10가지 키워드를 통해 주요 이슈를 도출하였고 각 토픽에 대한 2017년 1월부터 2018년 2월까지의 뉴스 데이터를 월별 시계열 분석을 통해 향후 토픽에 관한 이슈를 예측한다. 본 연구를 바탕으로 향후 건설 안전사고의 다양한 이슈를 선제적으로 예측하고 이를 기반으로 건설 안전사고 정책과 연구에 좋은 방향을 제시할 것으로 판단한다.

• 광산업정보
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• 통권53호
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• pp.14-15
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• 2009

• 한국주거학회논문집
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• 제2권2호
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• pp.41-53
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• 1991

This study discussed the change of housing environmental problems from the early 1900s to the present.The reason is to find the solution of serious housing environment problems. The documentary research method was used for this study.Articles of content analysis(N= 1129)were published in 1920(the first edition)to December. 31, 1990 which were The Dong - A daily news article about housing environment. The main content of this study was examined the change, such as the number of whole article by time series and importance of article(column number of article), classification of article subject, and the number of article by subject. On the basis of this data, was made by chronological classification of the change of housing environment problems for 70 years. Since overall results will become supply of right information about housing environment to fur peoples, will provide the oppronment that oneself ran participate the protection of housing environment, and further will take a part solution of housing environment problems.At the future, I am going to design deep analysis of article content by subject.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.627-630
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• 2022

Over the past few years, fake news has become one of the most significant problems. Since it is impossible to prevent people from spreading misinformation, people should analyze the news themselves. However, this process takes some time and effort, so the routine part of this analysis should be automated. There are many different approaches to this problem, but they only analyze the text and messages, ignoring the images. The fake news problem should be solved using a complex analysis tool to reach better performance. In this paper, we propose the approach of training an Artificial Intelligence using an unsupervised learning algorithm, combined with online data parsing tools, providing independence from subjective data set. Therefore it will be more difficult to spread fake news since people could quickly check if the news or article is trustworthy.

• 대한토목학회논문집
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• 제38권4호
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• pp.595-599
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• 2018

해외건설 프로젝트를 기획하고 수행하는 과정에서 현지 시장의 상황을 신속하고 정확하게 파악하는 것은 수익성 창출에 매우 큰 영향을 미친다. 뉴스기사 데이터는 정치, 경제, 사회 등 다양한 관한 정보를 담고 있기 때문에 시장의 상황을 파악하는 데 사용할 수 있는 좋은 데이터이다. 텍스트의 형태로 존재하는 대량의 뉴스기사 데이터로부터 정보를 추출하고 내용을 요약하는 과정에서 인력, 비용, 시간의 소모를 줄이기 위해 텍스트마이닝 기술이 필요하다. 본 연구에서는 뉴스기사에 다양한 주제가 공존한다는 특성으로 인해 발생하는 정보 추출의 한계를 극복하기 위해 잠재 디리클레 할당(Latent Dirichlet Allocation) 방법론을 사용하여 토픽 모델링을 수행했다. 문서 집단에 존재하는 주제의 개수가 10개라고 가정했을 때, 이용자들의 편의 증진을 위한 프로젝트(2번 주제)와 아프리카 지역의 빈곤 문제를 해결하기 위한 민간 차원의 지원(4번 주제) 등의 주제 집단이 존재하는 것을 확인했다. 이와 같이 문서 집단의 주제를 구분함으로써 더욱 의미있는 정보를 추출하고, 요약 결과의 활용성을 높일 수 있다.

• 한국문헌정보학회지
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• 제49권3호
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• pp.333-349
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• 2015

본 연구는 신문과 그 기사에 대한 FRBR 모형의 적용 방안을 검토하였다. 이를 위해 신문매체 수준과 신문기사 수준으로 나누어 데이터 요소를 분석하고 FRBR 모형을 어떻게 적용할 수 있는지에 대해 논하였다. 신문매체 수준의 경우, 신문, 조석간 구분, 호, 판을 각각의 저작으로 처리하였으며, 그들 간의 관계는 전체-부분 관계로 설정하였다. 신문기사 수준의 각각 기사도 저작이며, 이들은 신문매체 수준의 판과 전체-부분 관계를 가지고 있다. 신문기사는 텍스트, 사진, 그래픽, 표 등으로 표현되며, 각각 별도의 저작이다. 각각의 저작은 신문기사의 부분이 될 수도 있고, 독립하여 하나의 신문기사가 될 수도 있다. 또한, 고정물과 연재물의 통일 제목은 각각 저작으로 하였으며, 각각의 신문기사와 전체-부분 관계가 있다. 신문제호의 변경에 의해 하나의 묶음으로 하는 통일 제호는 슈퍼저작으로서 그들 간에도 전체-부분 관계가 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.4833-4848
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• 2019

This paper introduces the structure and effects of Kakao's news curation algorithm, which is created based on the Deep Reading Index (DRI). The DRI examines the extent of deep reading through content reading time, that is, the duration of reader engagement with an article. Current news curation algorithms focus on reader choice, with the click-through rate or pageviews as the gauge for consumption frequency. DRI is a product of the challenge of introducing and adopting a new factor called 'consumption time' instead of 'frequency of consumption', which is the basis of existing curation algorithms. The analysis of DRI-based services proves that the new algorithm can act as a curation system that is more effective in providing in-depth and quality news reports.