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

• International Journal of Advanced Culture Technology
• /
• v.9 no.3
• /
• pp.352-359
• /
• 2021

The purpose of this study is to design and build fake news discrimination systems and methods using fact-checking guidelines. In other words, the main content of this study is the system for identifying fake news using Artificial Intelligence -based Fact-checking guidelines. Specifically planned guidelines are needed to determine fake news that is prevalent these days, and the purpose of these guidelines is fact-checking. Identifying fake news immediately after seeing a huge amount of news is inefficient in handling and ineffective in handling. For this reason, we would like to design a fake news identification system using the fact-checking guidelines to create guidelines based on pattern analysis against fake news and real news data. The model will monitor the fact-checking guideline model modeled to determine the Fact-checking target within the news article and news articles shared on social networking service sites. Through this, the model is reflected in the fact-checking guideline model by analyzing news monitoring devices that select suspicious news articles based on their user responses. The core of this research model is a fake news identification device that determines the authenticity of this suspected news article. So, we propose news article identification methods with fact-checking guideline on Artificial Intelligence & Bigdata. This study will help news subscribers determine news that is unclear in its authenticity.

• International Journal of Advanced Culture Technology
• /
• v.9 no.3
• /
• pp.345-351
• /
• 2021

This study is designed to determine how to identify misleading news articles based on natural language processing on Artificial Intelligence & Bigdata. A misleading news discrimination system and method on natural language processing is initiated according to an embodiment of this study. The natural language processing-based misleading news identification system, which monitors the misleading vocabulary database, Internet news articles, collects misleading news articles, extracts them from the titles of the collected misleading news articles, and stores them in the misleading vocabulary database. Therefore, the use of the misleading news article identification system and methods in this study does not take much time to judge because only relatively short news titles are morphed analyzed, and the use of a misleading vocabulary database provides an effect on identifying misleading articles that attract readers with exaggerated or suggestive phrases. For the aim of our study, we propose news article identification methods in natural language processing on Artificial Intelligence & Bigdata.

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

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.5
• /
• pp.2087-2095
• /
• 2012

The editorial elements which are used in this paper are made up of news categories, photograph articles, titles of article written bold strokes and contents of article. Of these elements, only the three elements of photographic articles, the titles of article written bold strokes and contents of article had some effects on the choice of articles. For the portal news, only the news categories, the titles of article written bold strokes and the name of newspaper had an effect on the choice of articles. Of the news genres such as politics, business, social affairs, sports, culture/entertainment, world news and IT/science, only the three genres of social affairs, culture/entertainment and world news had some effects on news users' choice. For the difference between man group and woman group in analyzing the choice of articles, there was the difference in four elements of business, sports, culture/entertainment and IT/science.

• Journal of KIISE:Computing Practices and Letters
• /
• v.7 no.5
• /
• pp.416-428
• /
• 2001

In order to build a news archive system. the news video stream should be first segmented into several articles, ad their contents are abstracted effectively. This abstraction helps the users to understand the contents of the article without playing the whole video stream. This paper proposes a new article boundary detection scheme for the news video streams together with a new news article abstraction scheme using the shot types of the news video data. The shots in the news video are classified into anchor person shots, interview shots, speech shots, reporting shots, graphic shots, and others. Since the news article starts with an anchor shot whose duration is relatively longer than other shots with special screen structure, the article boundary in detected by the computing the length of the shot and checking the screen structure in the proposed scheme. For the effective abstraction of the article video, the graphic image located in the right-top of the anchor shot frames is primarily used in the proposed abstraction scheme since it is the abstraction of the article made by the producer of the news according to its contents so that it contains a lot of meaningful information. The key frames of the other shots except interview and report shots are also used to abstract the contents of the articles in the proposed scheme. Upon experimental results, the precision and recall values of the proposed article boundary detection scheme could be 92% and 96%, respectively. This paper also presents a design and implementation of a prototype news archive system on WWW that consists of an indexing tool, an authoring tool, a database for meta-data of the news, and a browsing tool.

• Journal of Digital Convergence
• /
• v.10 no.9
• /
• pp.253-262
• /
• 2012

This study tries to find the differences in attitude towards advertisement and corporate image according to the context of article in web contents, banner advertisement exposure and news type. The findings of this study are as follows. First, interaction effect between the context of Internet article on social contribution activity and attitude towards advertisement was confirmed. When the context of Internet article was negative, the news type that provides a photo with the article was more effective for the attitude towards advertisement. It is estimated that when a photo is provided with an Internet article for social contribution activity, the level of crowdedness increases and accordingly the negative context of the Internet article is cancelled out. Second, interaction effect between news type and advertisement type of social contribution activity article and attitude towards advertisement was confirmed. In the news type that provides a photo with an Internet article, corporate advertisement was more effective for attitude towards advertisement than product advertisement. Third, interaction effect between the news type of social contribution activity and corporate image according to advertisement type was confirmed. In the news type that provides a photo with an Internet article, product advertisement was more effective for corporate image than corporate advertisement.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.12
• /
• pp.569-578
• /
• 2021

Thanks to web development today, we can easily access online news via various media. As much as it is easy to access online news, we often face fake news pretending to be true. As fake news items have become a global problem, fact-checking services are provided domestically, too. However, these are based on expert-based manual detection, and research to provide technologies that automate the detection of fake news is being actively conducted. As for the existing research, detection is made available based on contextual characteristics of an article and the comparison of a title and the main article. However, there is a limit to such an attempt making detection difficult when manipulation precision has become high. Therefore, this study suggests using a verifying article to decide whether a news item is genuine or not to be affected by article manipulation. Also, to improve the precision of fake news detection, the study added a process to summarize a subject article and a verifying article through the summarization model. In order to verify the suggested algorithm, this study conducted verification for summarization method of documents, verification for search method of verification articles, and verification for the precision of fake news detection in the finally suggested algorithm. The algorithm suggested in this study can be helpful to identify the truth of an article before it is applied to media sources and made available online via various media sources.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.8
• /
• pp.1015-1021
• /
• 2020

Mobile news users want news articles without advertising, meanwhile the news providers require advertisement displays in several types to attain advertising revenue. In this paper, we classified the types of advertisements on mobile news articles into fixed article type which is fixed on some areas of articles, fixed screen type which is fixed on mobile screens, and a combination type of them. In addition, we proposed a smart solution based on overlapping method which effectively organize advertisements to not distract the readers. The proposed method is similar to fixed article type and overlapping technique of advertisements on news article's photo or virtual area. The performance evaluation result shows that the proposed method provides more spaces for news articles effectively than the existing methods. Although only some areas of advertisements may be blocked according to the number or size of advertisements, the effect is not critical.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.8
• /
• pp.999-1005
• /
• 2020

While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.