• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1111-1117
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• 2023

Recently, South Korean newspapers began trying to charge for online news. This study attempts to shed light on the factors that influence payment for online news by analyzing Korea Press Foundation's 2022 Media Audience Survey (N = 58,936). The results of this study showed a steady increase in past payment and paying intent for online news since 2020. Predictors of past payment for online news included gender, age, and education, and interest in political and social issues. News use through specific media (i.e., newspapers, magazines, portals, messengers, social media, video sites, and podcasts), as well as mobile applications and e-mail newsletters, were found to contribute to paid subscriptions. Based on the findings of the study, news organizations should prepare to offer differentiated news content through their own news platforms and establish concrete plans to build trust in news.

• Korean Management Science Review
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• v.31 no.4
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• pp.93-106
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• 2014

This paper investigated customers' willingness to pay (WTP) for the integrated news platform, which is a paid digital news service provided by Korean newspapers. The integrated news platform has been widely employed and regarded as an alternative to recover dramatically decreasing sales of newspapers since N-Screen era began. This study employed a conjoint analysis to examine WTP for the integrated news platform and its attributes. According to the results, WPT for the integrated news platform was estimated as 4543.6 won, which is only 30.3% of the real price. Digitalized newspaper and premium news were found to be significant attributes explaining customers' WTP. The results of this paper implies that present marketing strategies for the integrated news platform of Korean newspapers should be reconsidered and revised.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.249-258
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• 2019

Changes in media technolgy affect the competitive status of broadcasting networks as news media. The competitive media environment has pushed broadcasting network news programs to find new ways for leveling their qualitative performance up and rating. This study focuses on the empirical relationship between the two key value, news quality in terms of fairness and in-depthness and news ratings. This study is based on the analysis of broadcasting network news texts and individual news item raitngs. Empirical relationship between news quality factors and ratings was proved positive. But the relationship between the length of news item and rating was proved negative.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• 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.

• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.19-33
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• 2015

SNS is perceived as an effective tool for sharing news and enabling news content to reach many more users than before. And some users think that SNS is an important source to get news. This study's purpose is to understand the key factors contributing to behavior of news consumption on social network sites in Cambodia and its influence. We identified three key factors including convenience, recency, and variety; however, recency showed less significant effect on news consumption on SNS. Besides the key factors, it also seeks to understand the impact of news consumption on social engagement and government's transparency in Cambodia. The analytical results achieved through the Partial Least Squares (PLS) approach.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1375-1378
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• 2009

In this paper, a new method to efficiently generate the holographic news ticker in holographic 3DTV or 3-D movies using N-LUT method is proposed. The proposed method is largely consisted of five steps: construction of the LUT for each character, extraction of characters in news ticker, generation and shift of the CGH pattern for news ticker using the LUT, composition of hologram pattern for 3-D video and news ticker and reconstruct the holographic 3D video with news ticker. From some simulation results confirmed the feasibility of the proposed method in fast generation of CGH patterns for holographic news ticker.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.1-22
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• 2016

News contents has been collected, selected, edited and sometimes distorted by the news recommendation mechanisms of online portals in nowadays. Prior studies had not confirmed the consensus of newsworthiness, and they had not tried to empirically validate the impacts of newsworthiness on audience reactions. This study challenged to summarize the concepts of newsworthiness and validate the impact of representativeness of both editor's and audience's perspective on audience reactions as perceived news quality, trust on news portal, perceived usefulness, service satisfaction, loyalty, continuous usage intention, and word-of-mouth intention by adopting the representativeness heuristics method and information adoption model. 357 valid data had been collected using a scenario survey method. Subjects in each groups are exposed by 3 news recommendation mechanisms: 1) the time-priority news exposure mechanism (control group), 2) the reference-score-based news recommendation mechanism (a single treatment group), and 3) the major-news-priority exposure mechanism sorting by the reference scores made by peer audiences (the mixed treatment group). Data had been analyzed by the MANOVA and PLS method. MANOVA results indicate that only mixed method of both editor and audience recommendation mechanisms impacts on perceived news quality and trust. PLS results indicate that perceived news quality and trust could significantly affect on the perceived usefulness, service satisfaction, loyalty, continuance usage, and word-of-mouth intention. This study would contributions to empathize the role of information technology in media industry, to conceptualize the news value in the balanced views of both editors and audiences, and to empirically validate the benefits of news recommendation mechanisms in academy. For practice, the results of this study suggest that online news portals would be better to make mixed news recommendation mechanisms to attract audiences.

• Journal of Service Research and Studies
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• v.10 no.2
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• pp.59-76
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• 2020

The purpose of this study is to analyze the impacts of the lasciviousness of news anchor's mention on viewers'negative emotions, news reliability and their attitude towards the broadcasting company. This study also analyzed the moderating effect of anchor's attractiveness and viewers' sex. First, the more lascivious anchor's mention in news report gets, the more negative the viewers felt. Second, stronger lascivious expressions of news anchor's mention in news report had a negative effect on news reliability. Third, the moderating effect of the anchor attractiveness was found when news anchor's mention influences the viewers' attitude towards the broadcasting company : those who thought news anchor attractive showed less negative emotions and their news reliability and attitude towards the broadcasting company were higher compared to those who thought news anchor less attractive. Fourth, the moderating effect of the viewers' sex was found when news anchor's mention influences on viewers' negative emotions and the viewers' attitude towards the broadcasting company. This study has an academic and practical implication by studying the lasciviousness of news anchor's mention and anchor's attractiveness. This study is also a new approach of integrating the fields of journalism : News report and Anchor into the marketing fields : Attractiveness and Reliability. This can be meaningful for both journalism and marketing field.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.