• The Journal of Society for e-Business Studies
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• v.14 no.2
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• pp.41-56
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• 2009

The purpose of this paper is to present a statistical model that can predict the rapid shift of users' attention by analyzing the lifecycle patterns of news in a social news aggregation service. Internet news service sites have a distinct characteristic in a sense that users' attention change very quickly in a short period of time. In this research, we propose a regression model for each news category which can model the decay pattern of users' attention and the content promotion policy of a social news aggregator is proven to be a major source of the rapid growth in the popularity of news. The proposed model is expected to be useful for evaluation of the social news aggregation service provider's content promotion policy that attempts to maximize users' attention as well as the diversity of news contents.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.177-195
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• 2017

Traditionally, news was consumed only through printed newspapers and broadcasting media, such as radio and television. However, the Internet has enabled people to consume news content online. Since most of online news content has been provided for free, it is not easy for news providers to charge the fixed subscription fee for online news content. Therefore, as an alternative strategy, some online news providers have tried to adopt the Pay-What-You-Want (PWYW) pricing model, which allows users (readers) to pay as much as they want after consuming news content. As this pricing model shows some possibility to grow and replace the unsuccessful monetization strategy of online news content, we therefore examined the comparative importance of seven heuristic attributes (i.e., article evaluation, article share, article comment, article information design, article length, writer SNS, and writer information) affecting readers' voluntary payment behavior through a conjoint analysis with 379 news articles collected from online news Website (i.e., Ohmynews.com) where the PWYW model has been working successfully. This study found that article share and article length are the most important factors which affect online news content users' voluntary payment. Finally, two major and eight minor propositions are suggested based on the findings of the study. This study would suggest guidelines for how to create online news content which induces much more voluntary payment.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1313-1323
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• 2019

With the development of the Internet and the increase of smart phones, various services considering user convenience are increasing, so that users can check news in real time anytime and anywhere. However, online news is categorized by media and category, and it provides only a few related search terms, making it difficult to find related news related to keywords. In order to solve this problem, we propose a method to recommend related documents more accurately by applying Doc2Vec similarity to the specific keywords of news articles and weighting the title and contents of news articles. We collect news articles from Naver politics category by web crawling in Java environment, preprocess them, extract topics using LDA modeling, and find similarities using Doc2Vec. To supplement Doc2Vec, we apply TF-IDF to obtain TC(Title Contents) weights for the title and contents of news articles. Then we combine Doc2Vec similarity and TC weight to generate TC weight-similarity and evaluate the similarity between words using PMI technique to confirm the keyword association.

• 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 Information Management
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• v.43 no.4
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• pp.119-142
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• 2012

This study analyzed the characteristics of hyperlink service from news articles in chosun.com and washington Post sites. The study showed that the Washington Post revealed many hyperlink-related elements and an increase in interactivity while Chosun.com was not established consistent hyperlink service. Also, this study analyzed news distribution based on genre, as a result, the life section was occupied the highest proportion with 32.4% for Chosun.com, while the news sections were evenly distributed for the Washington Post. Finally, as a result of classifying the linked words into 3 categories(Informational, navigational, and transactional), the highest contents category in both Chosun.com and Washington Post turned out to be informational words. Theses results can be used to develop and provide the effective link service of news articles in internet newspaper sites.

• 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.

• Journal of Internet Computing and Services
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• v.3 no.5
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• pp.41-49
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• 2002

Web mining can be broadly defined as the discovery and analysis of useful information from the World Wide Web. In this paper. we tried to analyze a user access pattern and designed a system which can supply useful information to users through the web mining, The proposed system can search the information of users pattern through the web site and news letters, and pass through classification of category through filtering, The fuzzy association rules are applied to the users who access recently, to each category that generated though these processes, and compares the generated sets to each users-access pages set, and it can send appropriate news letter to each user.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1630-1648
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• 2021

Owing to the unprecedented COVID-19 pandemic, the pharmaceutical industry has attracted considerable attention, spurred by the widespread expectation of vaccine development. In this study, we collect relevant topics from news articles related to COVID-19 and explore their links with two South Korean pharmaceutical indices, the Drug and Medicine index of the Korea Composite Stock Price Index (KOSPI) and the Korean Securities Dealers Automated Quotations (KOSDAQ) Pharmaceutical index. We use generalized Dirichlet-multinomial regression (g-DMR) to reveal the dynamic topic distributions over metadata of index values. The results of our analysis, obtained using g-DMR, reveal that a greater focus on specific news topics has a significant relationship with fluctuations in the indices. We also provide practical and theoretical implications based on this analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• Journal of Contemporary Eastern Asia
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• v.12 no.2
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• pp.19-34
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• 2013

Previous research assessed media reporting on nuclear accidents and risks, whilst studies about the Fukushima accident focused on the impact of the Internet on coverage of the incident. However, little research has addressed news framing or comparisons of the perceptions of journalists in relation to reporting nuclear accidents. The aim of this study is to apply framing analysis to news content in The New York Times, the Los Angeles Times, and USA Today about the Fukushima accident. It explores the question of how journalists view reporting on complex events. Content analysis of these three newspapers shows that conflict, responsibility, and economic consequences were the most frequently used frames. According to the journalists interviewed, the biggest problem was the inability to assess information due to contrary positions held by experts. It is argued that the Fukushima accident was framed as a conflict of experts and officials' opinions, utility and government officials' responsibility, and economic consequences for the United States. Adherence to professional norms of objectivity and impartiality was signified as the best approaches to risk reporting.