• Journal of Korean Library and Information Science Society
• /
• v.50 no.4
• /
• pp.381-403
• /
• 2019

Information on newspaper articles were categorized into different topics, and each categories within different topics were developed into a faceted taxonomies model which was combined with fundamental facets. After suggesting the plan to construct such a model, the research of actual faceted taxonomies were conducted. Faceted taxonomies divide information on news articles into different topics(such as politics, economies and others) and combine fundamental facets with categories(for example, politics can be sub-classified into general politics, administration, legal system, and others) and sub-categories. Each sub-categories can be further subdivided. In taxanomies, categories can have hierarchical relationships. Categories-Facets, for example, can be utilized to combine "arts" with "people", "action", "event", "time", "place" and others. And Sub-category of the classification of "arts" such as "art," "music," "dance" form hierarchical relationships with "arts" and, in turn, can be used for browsing and further inferences. Furthermore, combining category and facets results in hierarchical structure in order of fundamental facets. As for the pilot vocabulary construction, faceted taxonomies of 145 words from news paper articles on the topic of "arts" were constructed using all construction elements covered in this study.

• Journal of Korean Academy of Nursing
• /
• v.53 no.1
• /
• pp.55-68
• /
• 2023

Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

• Journal of Korean Academy of Rural Health Nursing
• /
• v.18 no.2
• /
• pp.113-124
• /
• 2023

Purpose: The number of households vulnerable to isolation increases rapidly as social ties decrease, raising concerns about the associated increase in lonely deaths. This study aimed to identify issues related to lonely deaths by analyzing South Korean news articles; and to provide evidence for their use in preventing and managing lonely deaths via community nursing. Methods: This exploratory study analyzed the structure and trends of meaning of lonely deaths by identifying the association between keywords in news articles and lonely deaths. In this study, we searched for all news articles on lonely deaths, covering the period from January 1, 2010, to May 31, 2023. Data preprocessing and purification were conducted, followed by top-keyword extraction, keyword network analysis and topic modeling. The retrieved articles were analyzed using R and Python software. Results: Four main topics were identified: "discovering and responding to lonely death cases", "lonely deaths ending in lonely funerals", "supportive policies to prevent lonely deaths among of older adults", and "local government activities to prevent lonely deaths and support vulnerable populations." Conclusion: Based on these findings, it can be concluded that lonely death is a complex social phenomenon that can be prevented if society shows concern and care. Education related to lonely deaths should be included in nursing curricula for concrete action plans and professional development.

• International Journal of Contents
• /
• v.11 no.3
• /
• pp.54-62
• /
• 2015

In this paper, we investigate the impacts of the following four influential factors on flow in digital reading: contents, platforms, motivations, and places. The contents factor was subdivided into "news articles" and "journal papers"; platforms is comprised of "mobile phones," "tablets," and "laptops"; motivations consists of "pleasure" and "assignments"; and "home," "on the go," and "out of home" are the subdivisions of the places factor. We conducted a questionnaire survey with the study's participants and the following results are shown: 1) The flow during the reading of news articles is influenced by motivations, whereas the flow during the reading of journal papers is influenced by platforms. 2) Regarding mobile phones, motivations significantly affected the flow, whereas content types significantly affected the flow for tablets; also, laptops provided the best flow and articles can be read on the platform regardless of motivations. 3) Reading for pleasure rather than for assignments positively influenced the flow for all of the platforms. 4) With respect to news articles, the places providing flow are different across platforms. However, for journal papers, the places out of home provided good flow. For tablets, the places for flow significantly depended on the content type, which is not the case for laptops.

• The Journal of the Korea Contents Association
• /
• v.14 no.12
• /
• pp.683-693
• /
• 2014

This research examines a storytelling of a crisis presented in news coverages about Asiana Airline accident happened at San Francisco airport on July 6th, 2013. and investigate how the storytelling affects public's image toward an organization. To answer the research questions, qualitative content analysis of news articles (n=101) and survey (n=125) were employed. The subjects for the content analysis were Chosun Daily Newspaper and Hankyoreh Daily Newspaper, and the period of news articles is range from June 6th, 2013 to June 12th, 2014. The results showed that 10% out of total news articles was positive description toward stewardess who handled the crisis well in emergence situtation. The major expressions of depicting the stewardess's heroic activities in the news articles were tears, composure, hero, quick response, sacrifice, sorry, and so on. The online survey was conducted from Nov. 15, 2013 to Feb. 14, 2014 and found that the stewardess heroic activity influenced positively public's evaluation on Asian airline's crisis management and image of the company.

• Journal of Digital Convergence
• /
• v.16 no.11
• /
• pp.185-199
• /
• 2018

The purpose of this study is to derive the core technologies of VR using patent analysis and to explore the direction of social and public interest in VR using news analysis. In Study 1, we derived keywords using the frequency of words in patent texts, and we compared by company, year, and technical classification. Netminer, a network analysis program, was used to analyze the IPC codes of patents. In Study 2, we analyzed news articles using T-LAB program. TF-IDF was used as a keyword selection method and chi-square and association index algorithms were used to extract the words most relevant to VR. Through this study, we confirmed that VR is a fusion technology including optics, head mounted display (HMD), data analysis, electric and electronic technology, and found that optical technology is the central technology among the technologies currently being developed. In addition, through news articles, we found that the society and the public are interested in the formation and growth of VR suppliers and markets, and VR should be developed on the basis of user experience.

• The Journal of Information Systems
• /
• v.31 no.3
• /
• pp.131-152
• /
• 2022

Purpose The purpose of this study was to investigate the intention of using a news media platform using block chain through media company workers in a situation where various platform services using block chain are being newly released in the media industry. Therefore, in this paper, we intend to explore the development direction of the news media platform service using the block chain in the future by deriving implications through the characteristics of the block chain, user characteristics, and self-determination factors. Design/methodology/approach This study conducted a survey on the main characteristics of blockchain, user characteristics, self-determination, resistance to innovation, etc., and designed a research model by integrating factors on the continuity of intention to use the news media platform. Findings According to the empirical analysis result, in this study, it was confirmed that the intention to use the blockchain news media platform is significantly related to decentralization, which is a characteristic variable of the blockchain, perceived risk, which is a user characteristic variable, and competence and relationship, which is a self-determination variable. In addition, it was confirmed that it affects the perceived ease of use with respect to the intention to use. In addition, in this study, news writers write more careful articles as they cannot edit articles once written, which can contribute to improving the quality of news content.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.385-387
• /
• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• Atmosphere
• /
• v.30 no.3
• /
• pp.237-248
• /
• 2020

In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.3
• /
• pp.7-14
• /
• 2023

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.