• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.207-218
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• 2017

The purpose of this study is to analyze news content consumption of news consumers in which a few media conglomerates control news consumption market caused by deregulation of media policy and development of Internet communication technology. In doing so, this study analyzes the consumption realities of news consumers in the new news consumption market generated by new media and mobile communication technologies, and the effects how the new news consumption market influences on news consumption pattern of audiences. After surveyed 229 news consumers, this study founded that news consumers use NAVER(news portal site) mainly while consuming news contents, specifically younger generation tends to use NAVER heavily. Furthermore, it is founded that news consumers chose news outlets for consuming news contents not by the quality of news contents and the function of the news outlets but by their own convenience.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.241-255
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• 2013

In this study, the relationship between movie ticket reservation rates and Internet buzz share is analyzed. The correlations between movie ticket reservation rates and Internet buzz share in blogs, Internet cafes, news site, and Internet video in NAVER which is a representative Internet portal in Korea are analyzed empirically. The results show that there are positive correlations between buzz shares and movie ticket reservation rates. In particular, before movies at the box office, the correlations with Internet video is relatively higher than those of other channels, and after movies at the box office, the correlations with blogs and Internet cafe are relatively higher. Also, we can find that the correlations between Internet buzz shares on movies and movie ticket reservation rates are different depending on time lags and Internet channels.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.63-71
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• 2011

The current Web portal sites provide significant keywords with high popularity or importance; specifically, user-friendly services such as tag clouds and associated word search are provided. However, in general, since news articles are classified only with their date and categories, it is not easy for users to find other articles related to some articles while reading news articles classified with categories. And the conventional associated keyword service has not satisfied users sufficiently because it depends only upon user queries. This paper proposes a way of searching news articles by utilizing the keywords tightly associated with users' queries. Basically, the proposed method discovers a set of keyword association patterns by using the association rule mining technique that extracts association patterns for keywords by focusing upon sentences containing some keywords. The method enables users to navigate the space of associated keywords hidden in large news articles.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.33-55
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• 2022

This study analyzed the comments and the replies on internet news related to the presidential election in order to verify whether online discussions are properly conducted. According to Habermas' public sphere theory, discussions is an effort among participants to reach a social consensus through the deliberations that are based on open communications. We propose that if such discussions properly take place through the act of writing in the Internet space, the comments and the replies will show a certain difference in terms of the structure and the content. To validate, this study analyzed more than 40,000 comments collected from Daum News portal site in Korea. The topic of the related news was the presidential election, because it is a topic of which people are highly interested in and that comments are actively running. The result of the t-test and topic modeling result show that all the hypotheses were supported thus we conclude that online discussions properly took places. This study also showed that online comments are not chaotic remarks that relieve people's stresses, but rather an outcome of the deliberation processes moving towards a social consensus.

• Fashion & Textile Research Journal
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• v.13 no.6
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• pp.917-925
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• 2011

Flagship store opening is one of the most frequently reported retail news in which it is widely used to promote a new store opening. The purpose of this study was to explore the flagship stores on media reports. Flagship store cases were collected from the news article database of the largest internet portal in Korea. A total of 210 cases were collected and content-analyzed. Though various business types of flagship stores were observed, most were in fashion. The most common characteristic of the flagship cases was the location of the prime sites in a metropolitan city. Global luxury and designer fashion brands met most characteristics, but many flagship stores were not more than a brand-owned store. The flagship store term is emphasized with dramatic expressions and various promotional events for media interests and in turn for marketing communication effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• The Journal of Information Systems
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• v.29 no.3
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• pp.103-117
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• 2020

Purpose Recently, various issues related to toxic comments on web portal sites and SNS are becoming a major social problem. Toxic comments can threaten Internet users in the type of defamation, personal attacks, and invasion of privacy. Over past few years, academia and industry have been conducting research in various ways to solve this problem. The purpose of this study is to develop the deep learning modeling for toxic comments classification. Design/methodology/approach This study analyzed 7,878 internet news comments through CNN classification modeling based on Highway Network and OOV process. Findings The bias and hate expressions of toxic comments were classified into three classes, and achieved 67.49% of the weighted f1 score. In terms of weighted f1 score performance level, this was superior to approximate 50~60% of the previous studies.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.160-169
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• 2010

The mobile internet service market is growing very rapidly in Korea. This study outlines the current state of mobile internet services and analyzes the mobile contents types of media companies, especially newspapers, broadcasters and internet portal firms. As a result of this study, media companies provide generally different types of news and information for free and they are eager to develop the new mobile business models to maintain their power in new media. But mobile content distribution is still in the early stages due to the lack of clear business models. To satisfy the consumer's needs and to maintain the competitive advantages, the media companies should develop new business models and cooperate with other market participants to reduce the barriers in the mobile internet service market.

• Journal of Korean Clinical Nursing Research
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• v.25 no.3
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• pp.209-220
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• 2019

Purpose: A significant amount of public opinion about nurse bullying is expressed on the internet. The purpose of this study was to analyze the linkage structures among words extracted from comments on internet articles related to nurse workplace bullying using semantic network analysis. Methods: From February 2018 to April 2019, comments made on news articles posted to the Daum and Naver web portal containing keywords such as "nurse", "Taeum", and "bullying" were collected using a web crawler written in Python. A morphological analysis performed with Open Korean Text in KoNLPy generated 54 major nodes. The frequencies, eigenvector centralities, and betweenness centralities of the 54 nodes were calculated and semantic networks were visualized using the UCINET and NetDraw programs. Convergence of iterated correlations (CONCOR) analysis was performed to identify structural equivalence. Results: This paper presents results about March 2018 and January 2019 because these months had highest number of articles. Of the 54 major nodes, "nurse", "hospital", "patient", and "physician" were the most frequent and had the highest eigenvector and betweenness centralities. The CONCOR analysis identified work environment, nurse, gender, and military clusters. Conclusion: This study structurally explored public opinion about nurse bullying through semantic network analysis. It is suggested that various studies on nursing phenomena will be conducted using social network analysis.

• Journal of the Korean Society for information Management
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• v.23 no.1 s.59
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• pp.99-119
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• 2006

The most widely used information searching method in the current internet environment is the keyword-based one, which has certain limitations in terms of precision and recall. Most major internet portals provide directory-based searching as a means to complement these limitations. However, that they adopt different classification schemes brings significant inconvenience to the users, and it consequently suggests a need to develop mapping gateway to provide cross-portal, or cross-directory information searching. In this context, this study attempts to develop a prototype system of intermediary gateway for integrated search, using the directory services of three major portals, Naver, Yahoo and Empas, and test its performance.