• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.543-546
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• 2017

Recently, people are using the data analysis in order to follow the new trend in information and communication technology. Media plays an important role to expand the new issue in our society, especially affected to establish social awareness about science and technology. So, We find some major technologies (Machine Learning & Blockchains) of future communication and information based on the 200 news articles through two data analysis methods such as keyword analysis and sentiment analysis. We look forward this paper to constantly develop the technology of information and communication as the guiding frame of the new scientific world.

• Annual Conference of KIPS
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• 2014.11a
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• pp.890-893
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• 2014

IT산업의 새로운 패러다임으로 빅데이터 분석이 주요한 기술로 부각되고 있다. 본 논문에서는 빅데이터를 수집, 분석하여 이를 통해 피자 판매량을 예측하는 모델을 제안한다. 판매량 예측을 위하여 과거 판매 데이터와 함께 공휴일, 날씨, 뉴스기사, 경제지표, 트렌드, 스포츠 이벤트 등의 데이터를 수집하여 이용하였으며, 판매량 예측 방법으로는 회기분석과 인공신경망 학습 등을 사용하여 빅데이터를 사용하지 않은 경우와 정확도를 비교하였다. 실험 결과 빅데이터를 이용함으로써 예측 오차율이 5%이상 향상됨을 확인하였다.

• Smart Media Journal
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• v.7 no.4
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• pp.99-105
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• 2018

It is often the case that products or services on the market are causing problems, which hurt the business and image of the company. Responding appropriately to the problem and minimizing the damage is very important to business organizations. This study collected and analyzed the news articles related to the recall of the Galaxy Note 7, which was developed and launched by Samsung Electronics, one of the smartphone market leaders. Based on the issue lifecycle, the characteristics of the news were expressed by stages and the contents of the news were analyzed and visualized using association rules. The results of this study are expected to help business organizations to understand the changes and trends of issues and search for counter measures.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.16-21
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• 2022

사전학습 말뭉치는 위키백과 문서 뿐만 아니라 인터넷 커뮤니티의 텍스트 데이터를 포함한다. 이는 언어적 관념 및 사회적 편향된 정보를 포함하므로 사전학습된 언어 모델과 파인튜닝한 언어 모델은 편향성을 내포한다. 이에 따라 언어 모델의 중립성을 평가할 수 있는 지표의 필요성이 대두되었으나, 아직까지 언어 인공지능 모델의 정치적 중립성에 대해 정량적으로 평가할 수 있는 척도는 존재하지 않는다. 본 연구에서는 언어 모델의 정치적 편향도를 정량적으로 평가할 수 있는 지표를 제시하고 한국어 언어 모델에 대해 평가를 수행한다. 실험 결과, 위키피디아로 학습된 언어 모델이 가장 정치 중립적인 경향성을 나타내었고, 뉴스 댓글과 소셜 리뷰 데이터로 학습된 언어 모델의 경우 정치 보수적, 그리고 뉴스 기사를 기반으로 학습된 언어 모델에서 정치 진보적인 경향성을 나타냈다. 또한, 본 논문에서 제안하는 평가 방법의 안정성 검증은 각 언어 모델의 정치적 편향 평가 결과가 일관됨을 입증한다.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.169-177
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• 2009

In this paper, we propose a new shot boundary detection method which is optimized for news video story parsing. This new news shot boundary detection method was designed to satisfy all the following requirements: 1) minimizing the incorrect data in data set for anchor shot detection by improving the recall ratio 2) detecting abrupt cuts and gradual transitions with one single algorithm so as to divide news video into shots with one scan of data set; 3) classifying shots into static or dynamic, therefore, reducing the search space for the subsequent stage of anchor shot detection. The proposed method, based on singular value decomposition with incremental clustering and mercer kernel, has additional desirable features. Applying singular value decomposition, the noise or trivial variations in the video sequence are removed. Therefore, the separability is improved. Mercer kernel improves the possibility of detection of shots which is not separable in input space by mapping data to high dimensional feature space. The experimental results illustrated the superiority of the proposed method with respect to recall criteria and search space reduction for anchor shot detection.

• Korean journal of communication and information
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• v.58
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• pp.7-30
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• 2012

The advent of Social Journalism coincided with the rise of social media to create and deliver news information; as a type of civic journalism, social journalism may be characterized as a new form of information gathering and news reporting which is fed by citizens creating news information through their use social networking services (SNSs). The current study analyzed a Facebook page called, to determine how this page was utilized during the onset of the citizen movement for the Egyptian democratic revolution to produce news, to facilitate interaction among the public and to deliver the news under the form of networked journalism. Each post uploaded onto the Facebook page from January 27 till February 2, 2011 was coded in its category, content and the contextual frame of the news. The results of the study showed that during the first week, straight news rather than those with opinions was produced most frequently. The research findings of the current study suggest that in a society of political turmoil, such as in Egypt and other Arabic countries, when the institutionalized media are controlled severely by the government or other forces, SNSs can perform journalistic media roles which create and distribute news information representing facts and reality, and simultaneously facilitate the public's interactions on social and political issues.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• Convergence Security Journal
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• v.17 no.5
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• pp.111-119
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• 2017

As of March 2017, fake news is largely focused on political issues. Outside the country, main issues of the fake news have been a hot topic in the US presidential election in 2016 and emerged as a new political and social problem in countries like Germany and France. In Korea, issues of the fake news are also linked with political issues such as presidential impeachment and prosecution, impeachment quota, early election, etc. This phenomenon has recently led to the production and spread of fake news related to safety and security issues as well as political issues in connection with various methods of generating articles and sharing information. As a result, there is a high possibility that the information will be transformed into information that can cause considerable confusion to the public. Therefore, the recognition of such problems means that it is important at this point to consider the related situation analysis and effective countermeasures. To do this, we tried to make accurate and meaningful analysis for the diagnosis, analysis, forecasting and management of issues utilizing Big Data. As a result, it is found that the fake news is continuously generated in relation to the safety and security issue as well as the political issue in the South Korea, and differs from the general form occurring outside the country.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.53-77
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• 2012

This study analyses the difference of contents and tones of arguments among three Korean major newspapers, the Kyunghyang Shinmoon, the HanKyoreh, and the Dong-A Ilbo. It is commonly accepted that newspapers in Korea explicitly deliver their own tone of arguments when they talk about some sensitive issues and topics. It could be controversial if readers of newspapers read the news without being aware of the type of tones of arguments because the contents and the tones of arguments can affect readers easily. Thus it is very desirable to have a new tool that can inform the readers of what tone of argument a newspaper has. This study presents the results of clustering and classification techniques as part of text mining analysis. We focus on six main subjects such as Culture, Politics, International, Editorial-opinion, Eco-business and National issues in newspapers, and attempt to identify differences and similarities among the newspapers. The basic unit of text mining analysis is a paragraph of news articles. This study uses a keyword-network analysis tool and visualizes relationships among keywords to make it easier to see the differences. Newspaper articles were gathered from KINDS, the Korean integrated news database system. KINDS preserves news articles of the Kyunghyang Shinmun, the HanKyoreh and the Dong-A Ilbo and these are open to the public. This study used these three Korean major newspapers from KINDS. About 3,030 articles from 2008 to 2012 were used. International, national issues and politics sections were gathered with some specific issues. The International section was collected with the keyword of 'Nuclear weapon of North Korea.' The National issues section was collected with the keyword of '4-major-river.' The Politics section was collected with the keyword of 'Tonghap-Jinbo Dang.' All of the articles from April 2012 to May 2012 of Eco-business, Culture and Editorial-opinion sections were also collected. All of the collected data were handled and edited into paragraphs. We got rid of stop-words using the Lucene Korean Module. We calculated keyword co-occurrence counts from the paired co-occurrence list of keywords in a paragraph. We made a co-occurrence matrix from the list. Once the co-occurrence matrix was built, we used the Cosine coefficient matrix as input for PFNet(Pathfinder Network). In order to analyze these three newspapers and find out the significant keywords in each paper, we analyzed the list of 10 highest frequency keywords and keyword-networks of 20 highest ranking frequency keywords to closely examine the relationships and show the detailed network map among keywords. We used NodeXL software to visualize the PFNet. After drawing all the networks, we compared the results with the classification results. Classification was firstly handled to identify how the tone of argument of a newspaper is different from others. Then, to analyze tones of arguments, all the paragraphs were divided into two types of tones, Positive tone and Negative tone. To identify and classify all of the tones of paragraphs and articles we had collected, supervised learning technique was used. The Na$\ddot{i}$ve Bayesian classifier algorithm provided in the MALLET package was used to classify all the paragraphs in articles. After classification, Precision, Recall and F-value were used to evaluate the results of classification. Based on the results of this study, three subjects such as Culture, Eco-business and Politics showed some differences in contents and tones of arguments among these three newspapers. In addition, for the National issues, tones of arguments on 4-major-rivers project were different from each other. It seems three newspapers have their own specific tone of argument in those sections. And keyword-networks showed different shapes with each other in the same period in the same section. It means that frequently appeared keywords in articles are different and their contents are comprised with different keywords. And the Positive-Negative classification showed the possibility of classifying newspapers' tones of arguments compared to others. These results indicate that the approach in this study is promising to be extended as a new tool to identify the different tones of arguments of newspapers.

• Korean journal of communication and information
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• v.46
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• pp.449-484
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• 2009

It has been said that Gwangjudream, nevertheless a free press, plays a role as a local press that it should be, in a situation that other local papers do not. This study aims to reveal the forces that influence the article production of Gwangjudream, and to examine the interrelations between them, through using the methods of participant observations and depth interviews. In this course, it is eventually purpose of providing more deep understandings on the present circumstances and problems of the local papers and having a chance to concern the concrete ways to enhance them. This study results in revealing the five forces that primarily influence the article production of Gwangjudream: 1) as a historical force, keeping the spirit of the first publication that look forward to playing a role as a local press that it sound be, 2) as an individual force, the habitus of its members that is critical of mainstream society and culture, 3) as an organizational force, non-hierarchical culture and the independence of the editorial rights, 4) as a habitual force, the deny of beat system, 5) as an economical force, the power of sponsors, financial poorness, and the competition for attracting subscribers. While the historical force and the individual force play a role as fundamental circumstances and the organizational force and the habitual force as practical circumstances for producing articles, they encourage to emerge the characteristics of the articles that are related to citizens' everyday life and reflect locality, and criticize and keep an eye on government and other public offices. However, the economical force provides the circumstances that weaken the characteristics of Gwangjudream. The results of this study question the perspective to overly regard it as coming from their economical weakness that the local newspaper do not play a role as a local press that it should be.