• Journal of Information Technology Applications and Management
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• 제26권6호
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• pp.1-12
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• 2019

On the Internet age, the news is generated and distributed not only by traditional news media, but also by a variety of online news media, news platforms, content websites/content farms, and social media. Since it is an easy task to create and distribute news, some of these news reports may contain fake or false facts. In the end, the cyberspace is full of fake or false messages. People may wonder if these fake news actually influence our decision making. In this paper, we discussed a real case of fake news. In this case, a Taiwanese company used some fake news, advertorial news, and news placement to manipulate or influence its stock price and trade volume. We collected all news for the case company during a period of four years and five months (from January 2013 to May 2017). We analyzed the relationship between published news and stock price. Based on the analysis results, we conclude that we should not ignore the influence of news placement and fake business news on the stock price.

• International Journal of Advanced Culture Technology
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• 제9권3호
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• pp.352-359
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• 2021

The purpose of this study is to design and build fake news discrimination systems and methods using fact-checking guidelines. In other words, the main content of this study is the system for identifying fake news using Artificial Intelligence -based Fact-checking guidelines. Specifically planned guidelines are needed to determine fake news that is prevalent these days, and the purpose of these guidelines is fact-checking. Identifying fake news immediately after seeing a huge amount of news is inefficient in handling and ineffective in handling. For this reason, we would like to design a fake news identification system using the fact-checking guidelines to create guidelines based on pattern analysis against fake news and real news data. The model will monitor the fact-checking guideline model modeled to determine the Fact-checking target within the news article and news articles shared on social networking service sites. Through this, the model is reflected in the fact-checking guideline model by analyzing news monitoring devices that select suspicious news articles based on their user responses. The core of this research model is a fake news identification device that determines the authenticity of this suspected news article. So, we propose news article identification methods with fact-checking guideline on Artificial Intelligence & Bigdata. This study will help news subscribers determine news that is unclear in its authenticity.

• 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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제12권4호
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• pp.179-188
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• 2023

최근 가짜뉴스는 뉴스 콘텐츠 형식을 가장하고 중요한 사건이 발생할 때마다 등장하여 사회적 혼란을 초래한다. 이에 가짜뉴스를 탐지하기 위한 연구로 인공지능 기술이 사용된다. 자연어 처리를 통해 가짜뉴스를 자동으로 인지 및 차단하거나, 네트워크 인과 추론과 결합함으로써 허위 정보를 확산시키는 소셜미디어 인플루언스 계정을 감지하는 등의 가짜뉴스 탐지 접근법이 딥러닝을 통해 구현될 수 있었다. 그러나 가짜뉴스 탐지는 여러 자연어 처리 분야 중에서도 해결이 어려운 문제로 분류된다. 가짜뉴스가 가지는 형식 및 표현의 다양성으로 특성 추출의 난도가 높고, 뉴스가 속한 범주에 따라 하나의 특성이 서로 다른 의미를 가질 수도 있는 등 다양한 한계점이 존재한다. 본 논문에서는 가짜뉴스를 탐지하기 위한 추가적인 식별 기준으로 감성 변화 패턴을 제시한다. 합성곱 신경망을 가짜뉴스 데이터 세트에 적용하여 콘텐츠 특성에 기반한 분석을 수행하고, 감성 변화 패턴을 추가로 분석함으로써 성능이 개선된 모델을 제안한다. 뉴스를 구성하는 문장에 대하여 감성 극성을 산출하고 장단기 메모리를 적용함으로써 문장 순서에 의존적인 결괏값을 얻을 수 있다. 이를 감성 변화의 패턴으로 정의하고 뉴스의 콘텐츠 특성과 결합하여 가짜뉴스 탐지를 위한 제안 모델의 독립변수로 활용한다. 제안 모델과 비교 모델을 딥러닝으로 학습시키고 가짜뉴스 데이터 세트를 이용한 실험을 진행하여 감성 변화 패턴이 가짜뉴스 탐지 성능을 개선할 수 있음을 확인한다.

• 지역사회간호학회지
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• 제12권2호
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• pp.513-531
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• 2001

Television news programs are becoming significant source of health information. This study aims at investigating the current state of health coverage of the prime time news program in Korea. Data were collected from KBS 9 0'clock news in the period of thirteen months. from December 1. 1998. to November 1. 1999. The data were analyzed using content analysis method. and the reliability degree was 99.7% according to the Holsti's inter-coder reliability test. The current research classified 489 health related news items into 49 sub-categories and five health categories through content analysis. Some of the basic results of this study are as follows. 1. The frequency according to health category, health maintenance promotion(57.3%) topped followed by disease prevention(23.2%), disease treatment(14.9%), life ethics(4.0%), and growth development(0.6%). 2. According to human developmental age. for the most part(80.1 %) is applicable to the entire range of human developmental age. 3. Health maintennance promotion category take top of health category by the rate of 57.3% and contain 20 sub-categories. 4. News items in the life ethics category, which had six sub-categories. occupied only four percent of the total health related news. News in the growth development category included two sub categories and occupied 0.6% of the total news items. 5. In disease prevention and disease treatment category, infectious disease(33.2%) showed the highest percentage according to the WHO's international disease classification system. Disease prevention occupied 23.2% and contained eleven sub-categories while disease treatment occupied 14.9% and included ten sub-categories. Television news coverage on health showed a wide variety of selection in terms that they are reporting various issues. This study, however, found that some news items were confusing and failing in presenting scientific evidences. It is suggested that the television coverage on health could be beneficial to most of viewers in receiving important health information and guidelines, only if they are utilizing their own sound discretion in consuming those news.

• 방송공학회논문지
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• 제22권4호
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• pp.438-447
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• 2017

이 연구는 사행산업의 분야인 복권, 체육진흥투표권, 경마, 카지노에 대해 언론에서는 어떻게 다루어지고 있는지를 1990년부터 2015년까지의 뉴스데이터를 의미연결망 분석을 통해 밝혀보고자 하는 연구이다. 기사의 빈도와 연결성을 프레이밍과 시민관심 정도로 재조명 하여 기사에 대한 언론보도자의 의도와 시민의 인식차이를 밝히고, 이를 통해 정책적 특성과 개혁과제를 탐색하였다. 분석결과 복권의 경우 당첨번호, 당첨금, 조작의혹 등 당첨에 대한 부분이 주제인 '사회문제' 형태였으며, 체육진흥투표권의 경우에는 사업입찰, 불법사이트, 발매대상 등 주로 사업추진과 불법사이트에 대한 '의무정보' 종류였고, 경마의 경우 사업장, 홍보, 기사 등으로 사업홍보나 광고 관련 뉴스이었고, 마지막으로 카지노의 경우에는 불법, 도박장, 외국인 등 '주요정보'에 해당하는 논문이었다. 시대에 따라 1990년대에는 카지노, 2000년대에는 복권, 2010년대에는 경마에 대한 기사보도가 많아졌으며, 이에 대한 시민의 반응도 사업비리, 당첨, 시민운동 등의 차이가 있었다. 마지막으로 기사의 빈도와 연결성이 나타내는 프레이밍 정도와 시민의 관심은 '1. 홍보광고(경마), 2. 의무정보(스포츠베팅), 3. 사회이슈(복권), 4. 주요정보(카지노)' 네 가지로 구분되었으며 이 중 사고, 비리 등 주요기사로 구분되는 사회문제가 주요 공공의제로 형성되는 것을 확인할 수 있었다.

• 융합경영연구
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• 제10권3호
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• pp.21-36
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• 2022

Purpose: The purpose of this study is to compare and analyze the enterprise's score index calculated from atypical data and corrected data. Research design, data, and methodology: In this study, news articles which are non-financial information but qualitative data were collected from 2,432 SMEs that has been extracted "square proportional stratification" out of 18,910 enterprises with fixed data and compared/analyzed each enterprise's score index through text mining analysis methodology. Result: The analysis showed that qualitative data can be quantitatively evaluated by region, industry and period by collecting news from SMEs, and that there are concerns that it could be an element of alternative credit evaluation. Conclusion: News data cannot be collected even if one of the small businesses is self-employed or small businesses has little or no news coverage. Data normalization or standardization should be considered to overcome the difference in scores due to the amount of reference. Furthermore, since keyword sentiment analysis may have different results depending on the researcher's point of view, it is also necessary to consider deep learning sentiment analysis, which is conducted by sentence.

• 한국컴퓨터정보학회논문지
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• 제20권7호
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• pp.1-7
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• 2015

Although data on the internet can be used in various fields such as source of data of IR(Information Retrieval), Data mining and knowledge information servece, and contains a lot of unnecessary information. The removal of the unnecessary data is a problem to be solved prior to the study of the knowledge-based information service that is based on the data of the web page, in this paper, we solve the problem through the implementation of XTractor(XPath Extractor). Since XPath is used to navigate the attribute data and the data elements in the XML document, the XPath analysis to be carried out through the XTractor. XTractor Extracts main text by html parsing, XPath grouping and detecting the XPath contains the main data. The result, the recognition and precision rate are showed in 97.9%, 93.9%, except for a few cases in a large amount of experimental data and it was confirmed that it is possible to properly extract the main text of the news.

• 정보교육학회논문지
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• 제26권6호
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• pp.543-552
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• 2022

본 연구에서는 빅데이터 분석기법을 활용하여 통합교육에 대한 언론보도의 사회적 인식을 분석하고자 하였다. 특수교육 발전 5개년 정책 시기에 따라 관련 뉴스 기사를 수집하여, 뉴스 빅데이터를 분석하였다. 그 결과 1차년도 1998년부터 5차년도 2022년까지 특수교육 발전 5개년 정책기간의 언론보도 빈도는 꾸준히 증가한 것으로 나타났다. 이 시기 동안 언론보도의 상위 주제어는 단순한 정의를 개념화하는 단어들로부터 장애 당사자의 실질적교육권에 대한 적극적 의지를 드러내는 단어로 변화가 나타났다. 또한 통합교육 뉴스 기사의 전체적인 키워드 감성 분석 결과 긍정적인 단어 비율이 높은 것으로 나타났다. 본 연구를 통해 특수교육 정책 변화에 따라 통합교육에대한 언론보도의 관심이 양적으로 증가하고 통합교육의 요구가 장애 당사자의 실질적인 교육권을 보장하는 방향으로 구체화되고 있음을 알 수 있다.

• 한국IT서비스학회지
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• 제18권1호
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• pp.219-234
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• 2019

Fake news threaten democratic elections and causes social conflicts, resulting in major damage. However, the concept of fake news is hard to define, as there is a saying, "News is not fake, fake is not news." Fake news, however, has irreversible characteristics that can not be recovered or reversed completely through post-punishment of economic and political benefits. It is also rapidly spreading in the early days. Therefore, it is very important to preemptively detect these types of articles and prevent their blind proliferation. The existing countermeasures are focused on reporting fake news, raising the level of punishment, and the media & academia to determine the authenticity of the news. Researchers are also trying to determine the authenticity by analyzing its contents. Apart from the contents of fake news, determining the behavioral characteristics of the promoters and its qualities can help identify the possibility of having fake news in advance. The online community has a fake news interception and response tradition through its long-standing community-based activities. As a result, I attempted to model the fake news by analyzing the affirmation-denial analysis and posting behavior by securing the web board crawl of the 'M community' bulletin board during the 2017 Korean presidential election period. Random forest algorithm deemed significant. The results of this research will help counteract fake news and focus on preemptive blocking through behavioral analysis rather than post-judgment after semantic analysis.