• Journal of Information Science Theory and Practice
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• v.10 no.2
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• pp.1-16
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• 2022

This study aimed to understand young adults' attitudes concerning news and news resources they consumed, and how they encounter the fake news phenomenon. A qualitative approach was used with semi-structured interviews with 41 young adults (aged 20-30) in Tehran, Iran. Findings revealed that about half of the participants favored social media, and a smaller group used traditional media and only a few maintained that traditional and modern media should be used together. News quality was considered to be lower on social media than in traditional news sources. Furthermore, young adults usually followed the news related to the issues which had impact on their daily life, and they typically tended to share news. To detect fake news, they checked several media to compare the information; and profiteering and attracting audiences' attention were the most important reasons for the existence of fake news. This is the first qualitative study for understanding news consumption behavior of young adults in a politicized society.

• Journal of Distribution Science
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• v.21 no.7
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• pp.1-9
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• 2023

Purpose: This study aims to understand if heterogeneous news is evenly consumed and distributed on portals as it examines people's news use and engagement behaviors and news credibility. Focusing on the four behaviors of news use, i.e., viewing news by keyword search, viewing news from subscribed sources, viewing news from the list of most-viewed news, and reading comments, and the three behaviors of news engagement, i.e., sharing news, 'liking' or 'recommending' news, and posting comments, this study investigates the relation between each of the behaviors and portal news credibility. Research design, data and methodology: From 2022 News Audience Survey in Korea, this study conducts a regression analysis to investigate the relations between each behavior and news credibility. Results: The results show a positive relation for the former two news use behaviors and the latter two news engagement behaviors, and a negative relation for the latter two news use behaviors. Conclusions: The positive relations between active news use and engagement behaviors and portal news credibility indicate that news consumers are more likely to use and engage in attitude-consistent news rather than attitude-challenging news, implying that heterogeneous news is less likely to be consumed and distributed evenly on portals across all news users.

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.30
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• pp.6-9
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• 2008

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.29
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• pp.12-15
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• 2008

• 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.

• Journal of Information Science Theory and Practice
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• v.7 no.2
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• pp.40-53
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• 2019

Although fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic construction of filter bubbles, and (2) Are echo chambers of fake news manmade, and if yes, what are the information behavior patterns of those individuals reacting to fake news? We discuss the role of filter bubbles by analyzing social media's ranking and results' presentation algorithms. To understand the roles of individuals in the process of making and cultivating echo chambers, we empirically study the effects of fake news on the information behavior of the audience, while working with a case study, applying quantitative and qualitative content analysis of online comments and replies (on a blog and on Reddit). Indeed, we found hints on filter bubbles; however, they are fed by the users' information behavior and only amplify users' behavioral patterns. Reading fake news and eventually drafting a comment or a reply may be the result of users' selective exposure to information leading to a confirmation bias; i.e. users prefer news (including fake news) fitting their pre-existing opinions. However, it is not possible to explain all information behavior patterns following fake news with the theory of selective exposure, but with a variety of further individual cognitive structures, such as non-argumentative or off-topic behavior, denial, moral outrage, meta-comments, insults, satire, and creation of a new rumor.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• The Science & Technology
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• s.511
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• pp.104-107
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• 2011

• The Science & Technology
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• s.508
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• pp.96-99
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• 2011

• The Science & Technology
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• s.544
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• pp.104-107
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• 2014