• Journal of Korean society of Dental Hygiene
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• v.9 no.1
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• pp.59-71
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• 2009

Objectives : The purpose of this study is to devise a way of the dental hygienist to explore the articles of dental hygienist that were presented in the newspaper during the recent 3 years of Korea. Methods : This study is to examine dental hygienist articles with content analysis in the KINDS(Korean Integrated News Database System) of the Korean Press Foundation. Data were gathered from the printed newspaper of Korea over a period of 3 years - 1 March, 2005 to 30 March 2008. News reports, comments and letters to the editor were analysed, which revealed an image of dental hygienist that we would like to explore and debate. The obtained data from the frequency, percentage, chi-squared test between categories after inter-coder reliability test (reliability 0.96). Results : The articles of dental hygienist according to type of newspaper, 'local newspaper' showed higher frequency than 'metropolitan newspaper'. It mix '치과위생사'(42.3%), '치위생사'(49.4%), and '위생사'(3.9%) in use of name. The article pattern, 'news' 40.0%, 'information commentary' 18.3%, 'interview man' 15.8%, 'special news' 14.2% in metropolitan newspaper, then, 'news' 72.6%, 'information commentary' 23.2% in local newspaper (p<0.05). Most plenty of subject is 'administration system', and then 'celebration', 'publicity'. It showed 'seoul' was 'information commentary', 'country' was 'administration system', 'whole' was 'legal duty', 'unrelated area' was 'social living' in the topic of article according to newsbeat(p<0.05). Conclusions : These results suggest that it is necessary to publicity name, duty of dental hygienist in metropolitan newspaper officially.

• Smart Media Journal
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• v.6 no.1
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• pp.47-53
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• 2017

Social issues are important factors that decide government policy and newspapers are critical channels that reflect them. Analysing news articles can contribute to understanding social issues, but it is very difficult to analyse the unstructured large volumes of news data manually. Therefore, this study aims to analyze the different views among stakeholders of a specific social issue by using text analysis, word cloud analysis and associative analysis methods, which systematically transform unstructured news data into structured one. We analyzed a total of 115 news articles and a total of 6,772 comments, collected from the selected newspapers (Chosun-Il-bo, Joongang-Il-bo, Donga-Il-bo, Maeil Newspaper, Busan-Il-bo) for two weeks. We found that there are significant differences in tone between newspapers. While nation-wide daily newspapers focus on political relations with local areas, local daily newspapers tend to write articles to represent local governments' interests.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Information Science Theory and Practice
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• v.11 no.1
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• pp.1-13
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• 2023

This study determines how social media users (SMUs) verify the information they come across on the Internet. It determines SMUs' perception of online fact-checking services in terms of their ease of use, usefulness, and trust. By conducting a focus group discussion and key informant interviews, themes were derived in determining fact-checking practices while a survey was further conducted to determine such perceived ease of use, usefulness, and trust in fact-checking services. The thematic analysis revealed major information verification practices, such as cross-checking and verifying with other sources, inspecting comments and reactions, and confirming from personal and social networks. The results showed that SMUs considered fact-checking services easy to use. However, a concern was raised about their usefulness stemming from the delayed action in addressing the information issues that need to be verified. As to perceived trust, it was found that SMUs have reservations about fact-checking services. Finally, it is believed that fact-checking services are expected to be credible and need to be promoted to mitigate any form of fake news, particularly on social media platforms.

• Journal of Korean Academy of Nursing
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• v.53 no.4
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• pp.439-452
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• 2023

Purpose: This study aimed to derive considerations for the enactment of nursing law by analyzing the trends and social perceptions of nursing law mentioned in major daily newspapers, cafes, and blogs. Methods: Main texts and comments that included nursing law as a keyword were collected from major daily news and online postings from January 2021 to August 2022. The data collected through web crawling were analyzed using a TousFlux program used for big data analysis. Results: During the period of study, the awareness level around nursing law enactment increased. In particular, public concern over nursing law enactment intensified due to the two political parties' policy pledges related to nursing law in January 2022 and the failure to introduce the nursing law to the national assembly judiciary committee in May 2022. Except in December 2021, public perception of nursing law enactment was generally favorable, with public opinion tilting more in favor of than against enactment. Conclusion: Public opinion should be considered when drafting and implementing the nursing law to make it easier for the people to understand what the law constitutes. In addition, it is necessary to pay attention to and continuously promote the relationship between medical care and nursing in the nursing law system of developed nations. Lastly, nursing law enactment can enhance nurses' retention intention and provide a sense of efficacy to medical services.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.233-237
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• 2017

본 논문은 신문기사의 감성 댓글을 생성하기 위한 시스템을 제시한다. 감성을 고려한 댓글 생성을 위해 기존의 Sequence-to-Sequence 모델을 사용하여 긍정, 부정, 비속어 포함, 비속어 미포함 유형의 4개의 감성 모델을 구축한다. 하나의 신문 기사에는 다양한 댓글이 달려있지만 감성 사전과 비속어 사전을 활용하여 하나의 댓글만 선별하여 사용한다. 분류한 댓글을 통해 4개의 모델을 학습하고 감성 유형에 맞는 댓글을 생성한다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.233-237
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• 2017

본 논문은 신문기사의 감성 댓글을 생성하기 위한 시스템을 제시한다. 감성을 고려한 댓글 생성을 위해 기존의 Sequence-to-Sequence 모델을 사용하여 긍정, 부정, 비속어 포함, 비속어 미포함 유형의 4개의 감성 모델을 구축한다. 하나의 신문 기사에는 다양한 댓글이 달려있지만 감성 사전과 비속어 사전을 활용하여 하나의 댓글만 선별하여 사용한다. 분류한 댓글을 통해 4개의 모델을 학습하고 감성 유형에 맞는 댓글을 생성한다.

• Journal of Information Technology Applications and Management
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• v.14 no.3
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• pp.115-136
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• 2007

In this study, we investigate the possibility of deploying a recently emerging Internet-based technology, called Web log or Blog, to address the problems of knowledge transfer and sharing, particularly in the case of tacit knowledge. We examined the use practice of four blogs and then identified several properties relevant to knowledge transfer and sharing. They include the specific style of blog format, content ownership attribution, posted article organization, communication tools and method, news feed function, and various links from/to outside websites. These features were argued to facilitate knowledge transfer and sharing. In particular, we discussed a great deal about the structure of comments and links as tools for collaboration and idea sharing, which enables the knowledge conversion processes (socialization, externalization, combination, and internalization), We then provide several guidelines to develop blogs as a knowledge management tool.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.869-872
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• 2018

온라인 뉴스 소비의 확산과 함께 댓글은 여론 형성에 큰 역할을 담당한다. 그러나 아직 댓글에 영향을 미치는 형식 요소에 대한 실증 데이터 기반의 연구는 미흡하다. 본 연구는 이의 시작으로 온라인 뉴스 기사 소비의 두 가지 중요 요소 즉, 헤드라인과 댓글의 관계에 대해 다루고자 한다. 이를 위해, 헤드라인의 논조 유무에 따른 댓글의 논쟁 활성화 정도 차를 확인하고자 댓글의 수와 길이를 분석하였다. '이세돌, 알파고 바둑대결', '최저임금', '북미회담' 기사로 총 537건의 해드라인과 약 85만개의 댓글을 수집하였다. 그 결과 논쟁 활성화 측면에서 논조가 있는 헤드라인일때 댓글의 수가 많고 길이가 길어 논쟁이 더 활발한 것을 할 수 있었다. 또, 댓글의 논쟁 주제도 차이가 있어 헤드라인의 논조가 있는 경우에 의견이나 감정을 표출하는 토픽이 더 많았다. 본 연구는 실증 데이터를 통해, 헤드라인의 논조 유무가 댓글의 논쟁의 활성화 정도와 주제에 영향을 주는 요소임을 밝힘으로써 댓글 소비에 대한 새로운 관점을 제시하고, 헤드라인의 형식 요소의 연구의 중요성을 확인한 데 그 의의가 있다.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.