• International journal of advanced smart convergence
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• v.10 no.3
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• pp.131-142
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• 2021

Recently, there has been an active service that provides customized news to news subscribers. In this study, we intend to design a customized news service system through Deep Learning-based Social Network Service (SNS) activity analysis, applying real news and avoiding fake news. In other words, the core of this study is the study of delivery methods and delivery devices to provide customized news services based on analysis of users, SNS activities. First of all, this research method consists of a total of five steps. In the first stage, social network service site access records are received from user terminals, and in the second stage, SNS sites are searched based on SNS site access records received to obtain user profile information and user SNS activity information. In step 3, the user's propensity is analyzed based on user profile information and SNS activity information, and in step 4, user-tailored news is selected through news search based on user propensity analysis results. Finally, in step 5, custom news is sent to the user terminal. This study will be of great help to news service providers to increase the number of news subscribers.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.110-116
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• 2011

The production environment of fast and accurate news video has been continuously changing together with technology advance. Especially, the editing of the news video advanced focusing on the non-linear system by the introduction of digital technology and the recording of news video advanced to 'tapeless' base. In this new news video production environment, the recorded news videos are transmitted, edited and archived through the NPS (Network Production System), which dramatically improves the efficiency of the news video production. However, there are some issues which need to be resolved in the introduction of NPS. The purpose of this research is to identify the necessity of NPS introduction in the changing news video production environment, explore the issues which should be resolved and suggest the proper way to introduce the NPS to broadcasting companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4958-4970
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• 2019

As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.274-276
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• 2002

With the proliferation of internet, it is increasingly needed to realize personalized news filtering service reflecting user's interest. In this Paper, we implement a filtering agent for Personalized news service. In the proposed system, Kohonen network for an unsupervised learning is used to train keywords provided by users and the personalization is achieved by using the trained neural network. After we trained and tested our filtering agent we could provide users news groups considering their interests.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.278-291
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• 2016

This study examined research trends via network analyses of keywords appeared in academic research articles about news reports in South Korea during the last 10 years from 2006 to 2015. Keyword network analyses of 4410 keywords from 1108 articles suggested that framing, agenda setting, third-person effect, selective exposure, and uses and gratification were main theories but most studies used framing theory. Research areas included news reports on politics, economics, science, world issues, or tour. However, research on news reports covering culture, sports or daily life were not identified. In terms of media, research on both traditional and emerging media were ample. Research on broadcasting new, online news, and social media were frequently observed.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1119-1130
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• 2019

With the wide spread of Social Network Services (SNS), fake news-which is a way of disguising false information as legitimate media-has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and "Fasttext" which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.

• Journal of the HCI Society of Korea
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• v.9 no.2
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• pp.5-12
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• 2014

There was widespread criticism of the online news services due to their bias toward sensational and soft news. Thus, news services based on journalist values are socially requested. News source network analysis(NSNA), an algorithm to cluster and weight news sources, quotes, and articles, is suggested as a method to emphasize on journalist values like facts, variety, depth, and criticism in the previous study. This study suggests 'News Sources' as a visualization tool of NSNA. 'News Sources' shows news as bar graphs, weighted by facts and criticism, and arranged by organizations and subjects. This study designed a beta version using KINDS, a news archive of Korean Press Foundation.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.23-33
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• 2021

This study analyzed news articles about 'Young-Kl' reported by 11 media outlets, identifying news frames and quotation frames. Using a semantic network analysis, this study inspected the quotations frames and measured the frequency of the quotes and sources types. Also, the concentration index of the frames was measured. The results showed that news frames consisted of 10 topics and quotation frames consisted of 14 topics. Although the differences among quotation frames by media as well as by source types were observed, the concentration index of sources such as government, political arena, and business appeared high. Therefore, this study suggested that numerical diversity of news sources would not establish the diversity of news frames.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.103-119
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• 2018

This study explored news coverage on the sex crimes and analyzed news by network analysis and frame analysis based on the layered model to compare news coverage on the celebrity. As a result, in case of celebrity the broadcasting focused more and the tone of news is more sensational. The news in ground wave broadcasting more detailed on the sex crimes. It blamed the An, the governor of Chungnam more and the news is more sensational by interviewing marginal man. In #Metoo case, broadcasting news focused on the offender. The title of case name and the headline are framed based on the offender. Especially consensual relationship frame is dominated in the sex crime news. This study also can see the offender blaming frame and in the viewpoint of agenda-setting. It is difficult to find the cause of #Metoo movement and the structural approach on the case. This study highlighted the importance of layed model when analyzing the sex-crime news related with #Metoo movement.