• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.30-45
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• 2022

This study explored the issues of industrial mathematics in online news articles and online forums in Korea and the US by using text mining and compared the results. Text data about industrial mathematics were collected from news articles of Naver, a major portal site, and postings and replies on Clien as resources of Korea, and from news articles by the New York Times and CNN as well as postings and replies on Reddit as resources of the US. Structural topic modeling analyses were performed, the major results of which were as follows. First, news articles in Korea mainly dealt with the necessity of industrial mathematics and government support. On the contrary, the news articles in the US focused more on various fields where industrial mathematics fields were utilized. Second, in Korea, the same number of issues with different topics were discussed in news articles and online forums, whereas in the US more issues were covered in news articles than in online forums. It was suggested academic implications for researchers and practical implications for the government for settling industrial mathematics in Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1751-1754
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• 2022

The news articles collected with keyword "non face-to-face" were analyzed through topic modeling applied with LDA algorithm. In this study, collected articles were divided into two periods, period 1(the beginning of COVID-19 spread) and period 2(the end of COVID-19 spread), according to issued date of the articles. The articles of period 1 showed support for non-face-to-face treatment, smart library, the beginning of the online financial era, non-face-to-face entrance exam and employment, stock investment for main topic words. And the articles of period 2 showed conversion to non face-to-face classes, increasing unmanned stores, online finance, education industry, home treatment for main topic words. Also, further issues were discussed through visualization of topic words. These results provide evidence that education and unmanned business in non-face-to-face industries are growing.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.719-740
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• 2020

The stock market changes continuously as new information emerges, affecting the judgments of investors. Online news articles are valued as a traditional window to inform investors about various information that affects the stock market. This paper proposed new ways to utilize online news articles with technical indicators. The suggested hybrid model consists of three models. First, a self-attention-based convolutional neural network (CNN) model, considered to be better in interpreting the semantics of long texts, uses news content as inputs. Second, a self-attention-based, bi-long short-term memory (bi-LSTM) neural network model for short texts utilizes news titles as inputs. Third, a bi-LSTM model, considered to be better in analyzing context information and time-series models, uses 19 technical indicators as inputs. We used news articles from the previous day and technical indicators from the past seven days to predict the share price of the next day. An experiment was performed with Korean stock market data and news articles from 33 top companies over three years. Through this experiment, our proposed model showed better performance than previous approaches, which have mainly focused on news titles. This paper demonstrated that news titles and content should be treated in different ways for superior stock price prediction.

• Iberoamérica
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• v.16 no.2
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• pp.151-188
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• 2014

This work aims to describe pragmatic strategies and linguistic features of replies that occur in the comment section of online newspapers. The dominant media in this digital age is the Internet and its rapid development and expansion of use have contributed not only to the change of the form of production of journalistic texts, but also to the consumption of those texts. In the past, the news was transmitted in a unidirectional way but now readers of online newspapers do not remain passively reading the articles. They actively participate in the exchange of opinions with other readers. The individual consumption of journalistic texts has become a collective and social act. The purpose of the study is to investigate the communication intention of the users of comment sections and analyze the linguistic formulation of replies. We attempt to discover specific aspects of replies and responses for online newspaper articles, considering them as an independent type of Computer Mediated Communication (Internet Mediated Communication). Observing language attitudes appearing in the electronic environment and discovering the characteristics of the Spanish language on the Internet will allow us to contribute to understand the theoretical aspects related to the CMO better.

• Knowledge Management Research
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• v.14 no.4
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• pp.1-17
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• 2013

As online communities proliferate, online news sites have received great attention in news media research. Although most of the online news sites provide contents for free, some have adopted the Pay-What-You-Want (PWYW) model by offering a voluntary payment option to the readers. In this study, we investigate the factors which influence subscribers' voluntary payment behavior on an online news site. Drawing upon both the Stimulus-Organism-Response (SOR) framework and the Elaboration Likelihood Model (ELM), we hypothesize that appreciation has a direct effect on the subscribers' voluntary payment behavior, whereas central factors (positive emotional content, cognitive content) and peripheral factors (news sharing, news article length) of the news articles have indirect impacts on voluntary payment behavior through the enhanced appreciation. Based on an empirical analysis of 172 news articles from the Korean online news site that adopted the PWYW pricing model (i.e., Ohmynews.com), we find that appreciation plays a critical role in voluntary payment behavior and that peripheral factors have significant impacts on appreciation. However, the impacts of central factors on appreciation are not found. By identifying influencing factors of subscribers' voluntary payment behavior on online news sites for the first time, this paper suggests a prospective alternative profit model for online news providers faced with fierce competition.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.40-51
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• 2012

Online discussion bulletin in Korea is not only a specific place where user exchange opinions but also a public sphere through which users discuss and form public opinion. Sometimes, there is a heated debate on a topic and any article becomes a political or sociological issue. In this paper, we propose how to analyze the popularity of articles by collecting the information of articles obtained from two well-known discussion forums such as AGORA and SEOPRISE. And we propose a prediction model for the article popularity by applying the characteristics of subject articles. Our experiment shown that the popularity of 87.52% articles have been saturated within a day after the submission in AGORA, but the popularity of 39% articles is growing after 4 days passed in SEOPRISE. And we observed that there is a low correlation between the period of popularity and the hit count. The steady increase of the hit count of an article does not necessarily imply the final hit count of the article at the saturation point is so high. In this paper, we newly propose a new prediction model called 'baseline'. We evaluated the predictability for popular articles using three models (SVM, similar matching and baseline). Through the results of performance evaluation, we observed that SVM model is the best in F-measure and precision, but baseline is the best in running time.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.159-171
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• 2022

This study examined the problem of utilizing celebrity SNS in online news, which have increased by an average of 745 every year since 2010, reaching about 10,000 in 2021. 40 online newspapers were selected and 202,730 news articles produced by these newspapers in July 2021 were analyzed. As a result, 1.27% (2,582) of all articles were found to be using celebrity SNS as a source. This indicates that on average, online newspapers produce 2.08 celebrity SNS-utilized articles per day and 64.7 articles per month. Specifically, entertainer SNS (53.7%) was used the most compared to SNS of politician(39.8%) and influencer(6.5%). Instagram(69.1%, 57.1%) was utilized the most for entertainer and influencer and this were mostly related to personal information. On the other hand, Facebook(70.4%) was cited the most for politician, mostly related to opinions on social/political issues. The average length of SNS-based articles was 536 characters. The problem with news articles utilizing SNS is that most articles simply copy the SNS content without additional coverage(88.4%), and 14% of the articles did not disclose the exact source. Implication of the research on 40 online news agency is discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.2
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• pp.81-100
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• 2016

Much effort has been exerted to analyze online texts and understand how empirical results can help improve sales performance. In this research, we aim to extend this stream of research by decomposing online texts based on text sources, namely, companies and consumers. To be specific, we investigate how online texts driven by companies differ from those generated by consumers, and the extent to which both types of online texts have different effects on online sales. We obtained sales data from one of the biggest game publishers and merged them with online texts provided by companies using news articles and those created by consumers in user communities. The empirical analyses yield the following findings. Word visualization and topic analyses show that firms and consumers generate different contexts. Specifically, companies spread word to promote their own events whereas consumers produce online words to share winning strategies. Moreover, online sales are influenced by consumer-generated community topics whereas firm-driven topics in news articles have little to no effect. These findings suggest that companies should focus more on online texts generated by consumers rather than spreading their own words. Moreover, online sales strategies should take advantage of specific topics that have been proven to increase online sales. In particular, these findings give startup companies and small business owners in variety of industries the advantage when they use the online channel for distribution and as a marketing platform.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8195-8209
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• 2015

The purpose of this paper is to propose a method for recommendation and personalization of important news articles based on evaluating news value. Evaluation of news is the approach by which editors select news articles for cover-story in traditional offline news papers area. For this, my study proposes a suite of methods to select and personalize a set of news based on evaluating news articles, not just on the personal preference for them. The aforementioned the value of news articles including social impact, novelty, relevance to each audience, and human interest, all of which have been factorized in many previous studies, is a main concept for a procedural and structural application methodology deduced in this study. After a comparative case study with other online news services, it was shown that my research provides more effective way to select important news articles in terms of user satisfaction than others.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.75-100
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• 2018

Mobile devices have become an important channel for news content usage in our daily life. However, online news content readers' resistance to online news monetization is more serious than other digital content businesses, such as webtoons, music sources, videos, and games. Since major portal sites distribute online news content free of charge to increase their traffics, customers have been accustomed to free news content; hence this makes online news providers more difficult to switch their policies on business models (i.e., monetization policy). As a result, most online news providers are highly dependent on the advertising business model, which can lead to increasing number of false, exaggerated, or sensational advertisements inside the news website to maximize their advertising revenue. To reduce this advertising dependencies, many online news providers had attempted to switch their 'free' readers to 'paid' users, but most of them failed. However, recently, some online news media have been successfully applying the Pay-What-You-Want (PWYW) payment model, which allows readers to voluntarily pay fees for their favorite news content. These successful cases shed some lights to the managers of online news content provider regarding that the PWYW model can serve as an alternative business model. In this study, therefore, we collected 379 online news articles from Ohmynews.com that has been successfully employing the PWYW model, and analyzed the comparative importance of systematic attributes of online news content on readers' voluntary payment. More specifically, we derived the six systematic attributes (i.e., Type of Article Title, Image Stimulation, Article Readability, Article Type, Dominant Emotion, and Article-Image Similarity) and three or four levels within each attribute based on previous studies. Then, we conducted content analysis to measure five attributes except Article Readability attribute, measured by Flesch readability score. Before conducting main content analysis, the face reliabilities of chosen attributes were measured by three doctoral level researchers with 37 sample articles, and inter-coder reliabilities of the three coders were verified. Then, the main content analysis was conducted for two months from March 2017 with 379 online news articles. All 379 articles were reviewed by the same three coders, and 65 articles that showed inconsistency among coders were excluded before employing conjoint analysis. Finally, we examined the comparative importance of those six systematic attributes (Study 1), and levels within each of the six attributes (Study 2) through conjoint analysis with 314 online news articles. From the results of conjoint analysis, we found that Article Readability, Article-Image Similarity, and Type of Article Title are the most significant factors affecting online news readers' voluntary payment. First, it can be interpreted that if the level of readability of an online news article is in line with the readers' level of readership, the readers will voluntarily pay more. Second, the similarity between the content of the article and the image within it enables the readers to increase the information acceptance and to transmit the message of the article more effectively. Third, readers expect that the article title would reveal the content of the article, and the expectation influences the understanding and satisfaction of the article. Therefore, it is necessary to write an article with an appropriate readability level, and use images and title well matched with the content to make readers voluntarily pay more. We also examined the comparative importance of levels within each attribute in more details. Based on findings of two studies, two major and nine minor propositions are suggested for future empirical research. This study has academic implications in that it is one of the first studies applying both content analysis and conjoint analysis together to examine readers' voluntary payment behavior, rather than their intention to pay. In addition, online news content creators, providers, and managers could find some practical insights from this research in terms of how they should produce news content to make readers voluntarily pay more for their online news content.