• Journal of Information Technology Applications and Management
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• v.26 no.6
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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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• v.9 no.3
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Research in Community and Public Health Nursing
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• v.12 no.2
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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.

• Journal of Broadcast Engineering
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• v.22 no.4
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• pp.438-447
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• 2017

The purpose of this study is to understand the recognition of gambling industry through the semantic analysis of news data on lottery, sports betting, horse racing and casino that was reported between 1990 to 2015 in South Korea. This paper revealed the difference between journalists' intention and public's perception about news by analyzing the frequency and connectivity of news with framing and public's interest through semantic network analysis and explored the policy characteristics and innovation task. The result of analysis, news on lottery game mainly has been reported social issue related with win such as 'winning number', 'prize money', 'suspicion of manipulation' and etc. News on sports betting has been reported mandatory information related with business project and illegal site such as 'bidding', 'illegal site', 'sales target' and etc. News about horse racing has been reported the information about the business advertisement such as 'online race track' and 'promotion'. Lastly, casino related news has been reported 'major information' such as illegality', 'gambling place' and 'foreigner'. As a result of times series analysis, news about casino in the 1990s, news about lottery in the 2000s and news about horse racing in 2010s have been increased. Public's interest also has been moved to 'business scandal', 'winning game', 'citizens' campaign' and etc. Gambling related news has been classified by four types, 1. advertising publicity(horse racing), 2. mandatory information(sports betting), 3. social issue(public agenda, lottery), 4. major information(casino). We could get the insight that news can be formed a public agenda, when news is reported as a social issue with high frequency and public's interest like lottery related news.

• The Journal of Economics, Marketing and Management
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• v.10 no.3
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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.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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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.

• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.543-552
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• 2022

This study tried to analyze the social perception of news coverage on inclusive education by using big data analysis techniques. News articles were collected according to the 5-year policy period for the development of special education, and news big data was analyzed. As a result, the frequency of media reports during the five-year policy period of special education development from 1998 in the first year to 2022 in the fifth year was steadily increased. During this period, the top topic words in news coverage changed from words conceptualizing simple definitions to words expressing the active will of students with disabilities for the actual right to education. In addition, as a result of emotional analysis of the overall keywords in the inclusive education news coverage, it was found that the positive word ratio was high. Through this study, it can be seen that interest in news coverage on inclusive education is increasing quantitatively in accordance with changes in special education policies, and the demand for inclusive education is being concreted in the direction of guaranteeing the actual right to education of students with disabilities.

• Journal of Information Technology Services
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• v.18 no.1
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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.