• The Journal of Information Systems
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• v.31 no.3
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• pp.131-152
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• 2022

Purpose The purpose of this study was to investigate the intention of using a news media platform using block chain through media company workers in a situation where various platform services using block chain are being newly released in the media industry. Therefore, in this paper, we intend to explore the development direction of the news media platform service using the block chain in the future by deriving implications through the characteristics of the block chain, user characteristics, and self-determination factors. Design/methodology/approach This study conducted a survey on the main characteristics of blockchain, user characteristics, self-determination, resistance to innovation, etc., and designed a research model by integrating factors on the continuity of intention to use the news media platform. Findings According to the empirical analysis result, in this study, it was confirmed that the intention to use the blockchain news media platform is significantly related to decentralization, which is a characteristic variable of the blockchain, perceived risk, which is a user characteristic variable, and competence and relationship, which is a self-determination variable. In addition, it was confirmed that it affects the perceived ease of use with respect to the intention to use. In addition, in this study, news writers write more careful articles as they cannot edit articles once written, which can contribute to improving the quality of news content.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1419-1426
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• 2015

Distinguishing border between news and advertising is disappearing. Traditional journalism considered editorial part deals news and ad part handle commercial messages. But now this classification is meaningless. Current news consumers do not separate advertising content and non-advertising content. In Korea, making fake news or paid news pages is becoming social problem. Fake news uses various camouflages to pretend to be real news. This paper descriptively analyzed Korean fake news cases and suggested some guidelines for publishing news. We analyzed 3 major newspaper web sites from July to September, 2014. These three newspapers publish section pages everyday containing fake news or sponsored news. Totally more than one thousand articles were selected for content analysis. We coded the numbers of fake news, day of the week, the rate of sponsored news, average fake news publication number per pages, the conformity between news and advertising, and the type of fake news. We also coded the number of sponsored news article in day sections. We used method of comparing the advertising contents and news articles. As a result, 24.8% of news article were published for the advertising sponsors. Advertorial or fake news were sometimes arranged same pages the same day. We coded the conformity between same advertising and news content. More than 60 percent (60.9%) of fake news match with their sponsors. PR style of fake news is top and advertising type of fake news is the lowest.

• Atmosphere
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• v.30 no.3
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• pp.237-248
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• 2020

In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.181-194
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• 2009

With the increased interest on the document copyright, many of researches related to the document plagiarism have been done up to now. The plagiarism problem of newspaper articles has attracted much interest because the plagiarism cases of the articles having much commercial values in market are currently happened very often. Many researches related to the document plagiarism have been so hard to be applied to the newspaper articles because they have strong real-time characteristics. So to detect the plagiarism of the articles, many human detectors have to read every single thousands of articles published by hundreds of newspaper companies manually. In this paper, we firstly sorted out the articles with high possibility of being copied by utilizing OpenAPI modules supported by web search companies such as Naver and Daum. Then, we measured the document similarity between selected articles and the original article and made the system decide whether the article was plagiarized or not. In experiment, we used YonHap News articles as the original articles and we also made the system select the suspicious articles from all searched articles by Naver and Daum news search services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.345-351
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• 2020

Administrative agencies today are paying keen attention to big data analysis to improve their policy responsiveness. Of all the big data, news articles can be used to understand public opinion regarding policy and policy issues. The amount of news output has increased rapidly because of the emergence of new online media outlets, which calls for the use of automated bots or automatic document classification tools. There are, however, limits to the automatic collection of news articles related to specific agencies or departments based on the existing news article categories and keyword search queries. Thus, this paper proposes a method to process articles using classification glossaries that take into account each agency's different work features. To this end, classification glossaries were developed by extracting the work features of different departments using Word2Vec and topic modeling techniques from news articles related to different agencies. As a result, the automatic classification of newspaper articles for each department yielded approximately 71% accuracy. This study is meaningful in making academic and practical contributions because it presents a method of extracting the work features for each department, and it is an unsupervised learning-based automatic classification method for automatically classifying news articles relevant to each agency.

• Journal of Korean Library and Information Science Society
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• v.50 no.4
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• pp.381-403
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• 2019

Information on newspaper articles were categorized into different topics, and each categories within different topics were developed into a faceted taxonomies model which was combined with fundamental facets. After suggesting the plan to construct such a model, the research of actual faceted taxonomies were conducted. Faceted taxonomies divide information on news articles into different topics(such as politics, economies and others) and combine fundamental facets with categories(for example, politics can be sub-classified into general politics, administration, legal system, and others) and sub-categories. Each sub-categories can be further subdivided. In taxanomies, categories can have hierarchical relationships. Categories-Facets, for example, can be utilized to combine "arts" with "people", "action", "event", "time", "place" and others. And Sub-category of the classification of "arts" such as "art," "music," "dance" form hierarchical relationships with "arts" and, in turn, can be used for browsing and further inferences. Furthermore, combining category and facets results in hierarchical structure in order of fundamental facets. As for the pilot vocabulary construction, faceted taxonomies of 145 words from news paper articles on the topic of "arts" were constructed using all construction elements covered in this study.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.6
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• pp.871-879
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• 2010

In this paper, we present a system that classifies comments on a news article into a user opinion called a polarity (positive or negative). The system is a kind of document classification system for comments and is based on machine learning techniques like support vector machine. Unlike normal documents, comments have their body that can influence classifying their opinions as polarities. In this paper, we propose a feature weighting scheme using such characteristics of comments and several resources for opinion classification. Through our experiments, the weighting scheme have turned out to be useful for opinion classification in comments on Korean news articles. Also Korean character n-grams (bigram or trigram) have been revealed to be helpful for opinion classification in comments including lots of Internet words or typos. In the future, we will apply this scheme to opinion analysis of comments of product reviews as well as news articles.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.365-374
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• 2018

This study aims at understanding the components of on-line newspaper and how each newspaper's layout configuration is differentiated through the analysis on the websites of 10 general daily newspapers in the news stand of NAVER. The collection of data was implemented twice, and One-Way ANOVA was used as an analyzing way. The content of the analysis was carried out based on the types of visual images, the number of photo-based articles and title-based articles, the size of image for the main story, etc. As a result of analysis, the rate of news articles with the audios, videos, cards and slides differentiated from paper-based newspaper was low, and also the news articles using the informative graphics and the graphic sources were very small in number. As a whole, the newspapers in the news stand of NAVER showed that they attempt to make a distinction of their newspaper layout by using a variety of editorial techniques. The significance of this paper is to offer a basic clue to the editing formation to promote the news consumption of newspapers. Under the circumstance that the ecology of media is rapidly being reformed by new media technology, the continuous study of how the newspaper layout should be changed will be needed.