• Title/Summary/Keyword: online news

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A Study on the Relationships among SNS Characteristics, Satisfaction and User Acceptance

  • Ko, Changbae;Yoon, Jongsoo
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.11
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    • pp.143-150
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    • 2015
  • Social network services can be defined as an individual web page which enables online, human-relationship building by collecting useful information and sharing it with specific or unspecific people. Recently, as the social network services(SNS) such as Twitter and Facebook have been paid attention in many fields of the society. SNSs are also one of the fastest channels to get news which people may not be able to see on TV or newspaper. The number of people who feel they are benefiting from social network services are increasing dramatically. A number of researches about SNS are underway. The study based on the Technology Acceptance Model empirically investigates the relationship between characteristics of SNS (system, service, information, and emotional) and user satisfaction of SNS. The study also analyzes how the relationshipa between SNS characteristics, satisfaction and user acceptance are moderated by country type of SNS users and inclination toward SNS acceptance. To achieve these research purposes, the study conducted various statistical analyses using questionnaire of the Korean and Chinese SNS users. The results of the study are followings. First, SNS characteristics have a positive effect to the user satisfaction. Second, SNS satisfaction have a positive effect to the user acceptance. Third, the relationship between SNS characteristics and user satisfaction is moderated by the country type of SNS users and inclination toward SNS acceptance. The study results could provide some implications to researchers who have interest in studying SNS, also could help business managers to operate and develop their SNS site more effectively.

TRIB : A Clustering and Visualization System for Responding Comments on Blogs (TRIB: 블로그 댓글 분류 및 시각화 시스템)

  • Lee, Yun-Jung;Ji, Jung-Hoon;Woo, Gyun;Cho, Hwan-Gue
    • The KIPS Transactions:PartD
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    • v.16D no.5
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    • pp.817-824
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    • 2009
  • In recent years, Weblog has become the most typical social media for citizens to share their opinions. And, many Weblogs reflect several social issues. There are many internet users who actively express their opinions for internet news or Weblog articles through the replying comments on online community. Hence, we can easily find internet blogs including more than 10 thousand replying comments. It is hard to search and explore useful messages on weblogs since most of weblog systems show articles and their comments to the form of sequential list. In this paper, we propose a visualizing and clustering system called TRIB (Telescope for Responding comments for Internet Blog) for a large set of responding comments for a Weblog article. TRIB clusters and visualizes the replying comments considering their contents using pre-defined user dictionary. Also, TRIB provides various personalized views considering the interests of users. To show the usefulness of TRIB, we conducted some experiments, concerning the clustering and visualizing capabilities of TRIB, with articles that have more than 1,000 comments.

An Exploratory Study on the Policy for Facilitating of Health Behaviors Related to Particulate Matter: Using Topic and Semantic Network Analysis of Media Text (미세먼지 관련 건강행위 강화를 위한 정책의 탐색적 연구: 미디어 정보의 토픽 및 의미연결망 분석을 활용하여)

  • Byun, Hye Min;Park, You Jin;Yun, Eun Kyoung
    • Journal of Korean Academy of Nursing
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    • v.51 no.1
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    • pp.68-79
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    • 2021
  • Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

Big Data Analysis on Daegu-Gyeongbuk Administrative Integration (대구·경북 행정통합에 대한 빅데이터 분석)

  • Song, Hwa Young;Park, Han Woo
    • The Journal of the Korea Contents Association
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    • v.21 no.5
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    • pp.139-148
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    • 2021
  • The study examines public attitude and reaction regarding administrative integration in Daegu and Gyeongbuk area. Specifically, it employs social big data including textual comments on online news articles and YouTube video clips. The collected data are analyzed in order to compare two periods, that is, before and after the inauguration of the Public Opinion Committee for One Daegu-Gyeongbuk. As a result, we have found that people's favorable response to administrative integration has gradually increased since the launch of the Committee. However, it still lacks specific administrative procedures and discussion topics among the frequently used words in the collected data. Thus, the Committee needs to provide a variety of information and materials related to administrative integration.

Analyzing Predictors of Gamer Issue Participation: Focused on the Role of Media Source, Corrective Action, and Attitudinal Information (게이머 이슈 참여에 미치는 영향 연구: 미디어 출처, 시정 행동과 태도 정보의 역할을 중심으로)

  • Jung, Chang Won
    • The Journal of the Korea Contents Association
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    • v.22 no.2
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    • pp.187-197
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    • 2022
  • This study examined the effects of game genre, news media with differing political ideologies, and game-related information sources on gamer issue participation by performing a hierarchical regression model, using an online survey on Korean gamers (N=1,362). As a result of the study, playing specific genres of games played a positive role in gamer issue participation. The group behavior or collective action for or against game regulation reported in the liberal/moderate media acted as a mobilization cue for readers and potentially encouraged gamers to take social action. But the conservative media, which used governmental organizations and interest groups as sources of information, had a negative impact on real-life participatory behavior. The biased journalism practice of the mass media on game-related social issues influenced gamers' social and political behavior through corrective action. This study is significant in empirically analyzing the relationship between political ideology, game genre, media use, and gamers' social participation. The current research suggests the improvement of game regulation policy and the need for theoretical and conceptual expansion of game research.

Information Politics of Ukraine in the Field of Freedom of Conscience in a Pandemic

  • Mykola, Palinchak;Dobrodum, Olga;Khrypko, Svitlana;Gold, Olga;Ostashchuk, Ivan;Vlasenko, Inna;Lobanchuk, Olena
    • International Journal of Computer Science & Network Security
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    • v.22 no.11
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    • pp.222-228
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    • 2022
  • In today's era of digital technologies, the problem of religious communication in the cyberspace is being actualized, since the globality and accessibility of the WWW makes it one of the most effective and promising channels for transmitting various kinds of messages, including those of a religious nature. Today, religious organizations and movements pay the closest attention to the virtual media space, not only using it to attract new followers, but also for religious PR, image-making and branding, informing the world about themselves through news from the life of the organization and its followers. An equally important form of electronic communication in the online sphere is currently the interaction of various religious movements and religious cultures in general, or the dialogue of confessions in particular. Research in the digital space makes it possible to identify important trends in religious spheres based on the analysis of the flow of information on the Internet, to demonstrate the specifics of individual media outlets and the consequences of their activities for interreligious dialogue, to study the role of the Internet in changing religious beliefs, the possibility of changing religious identity, retrospective development of religious enlightenment at the turn of the century, to determine the vectors of possible interreligious interaction and discuss the role of digital technologies in the work of religious structures, to state the need to continue an active dialogue between representatives of religious movements, to hold expert seminars on interreligious dialogue on a regular basis, and to record the risks generated by the digital space. Thus, the coronavirus pandemic served as a background and context, a litmus test and a catalyst for accelerating and intensifying interreligious, interfaith dialogue and dialogue between religious organizations and society.

Korean Hedge Detection Using Word Usage Information and Neural Networks (단어 쓰임새 정보와 신경망을 활용한 한국어 Hedge 인식)

  • Ren, Mei-Ying;Kang, Sin-jae
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.7 no.9
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    • pp.317-325
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    • 2017
  • In this paper, we try to classify Korean hedge sentences, which are regarded as not important since they express uncertainties or personal assumptions. Through previous researches to English language, we found dependency information of words has been one of important features in hedge classification, but not used in Korean researches. Additionally, we found that word embedding vectors include the word usage information. We assume that the word usage information could somehow represent the dependency information. Therefore, we utilized word embedding and neural networks in hedge sentence classification. We used more than one and half million sentences as word embedding dataset and also manually constructed 12,517-sentence hedge classification dataset obtained from online news. We used SVM and CRF as our baseline systems and the proposed system outperformed SVM by 7.2%p and also CRF by 1.2%p. This indicates that word usage information has positive impacts on Korean hedge classification.

Analysis of Regional Fertility Gap Factors Using Explainable Artificial Intelligence (설명 가능한 인공지능을 이용한 지역별 출산율 차이 요인 분석)

  • Dongwoo Lee;Mi Kyung Kim;Jungyoon Yoon;Dongwon Ryu;Jae Wook Song
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.47 no.1
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    • pp.41-50
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    • 2024
  • Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

Summarization of Korean Dialogues through Dialogue Restructuring (대화문 재구조화를 통한 한국어 대화문 요약)

  • Eun Hee Kim;Myung Jin Lim;Ju Hyun Shin
    • Smart Media Journal
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    • v.12 no.11
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    • pp.77-85
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    • 2023
  • After COVID-19, communication through online platforms has increased, leading to an accumulation of massive amounts of conversational text data. With the growing importance of summarizing this text data to extract meaningful information, there has been active research on deep learning-based abstractive summarization. However, conversational data, compared to structured texts like news articles, often contains missing or transformed information, necessitating consideration from multiple perspectives due to its unique characteristics. In particular, vocabulary omissions and unrelated expressions in the conversation can hinder effective summarization. Therefore, in this study, we restructured by considering the characteristics of Korean conversational data, fine-tuning a pre-trained text summarization model based on KoBART, and improved conversation data summary perfomance through a refining operation to remove redundant elements from the summary. By restructuring the sentences based on the order of utterances and extracting a central speaker, we combined methods to restructure the conversation around them. As a result, there was about a 4 point improvement in the Rouge-1 score. This study has demonstrated the significance of our conversation restructuring approach, which considers the characteristics of dialogue, in enhancing Korean conversation summarization performance.

Exploring the phenomenon of veganphobia in vegan food and vegan fashion (비건 음식과 비건 패션에서 나타난 비건포비아 현상에 대한 탐구)

  • Yeong-Hyeon Choi;Sangyung Lee
    • The Research Journal of the Costume Culture
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    • v.32 no.3
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    • pp.381-397
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    • 2024
  • This study investigates the negative perceptions (veganphobia) held by consumers toward vegan diets and fashion and aims to foster a genuine acceptance of ethical veganism in consumption. The textual data web-crawled Korean online posts, including news articles, blogs, forums, and tweets, containing keywords such as "contradiction," "dilemma," "conflict," "issues," "vegan food" and "vegan fashion" from 2013 to 2021. Data analysis was conducted through text mining, network analysis, and clustering analysis using Python and NodeXL programs. The analysis revealed distinct negative perceptions regarding vegan food. Key issues included the perception of hypocrisy among vegetarians, associations with specific political leanings, conflicts between environmental and animal rights, and contradictions between views on companion animals and livestock. Regarding the vegan fashion industry, the eco-friendliness of material selection and design processes were seen as the pivotal factors shaping negative attitudes. Furthermore, the study identified a shared negative perception regarding vegan food and vegan fashion. This negativity was characterized by confusion and conflicts between animal and environmental rights, biased perceptions linked to specific political affiliations, perceived self-righteousness among vegetarians, and general discomfort toward them. These factors collectively contributed to a broader negative perception of vegan consumption. In conclusion, this study is significant in understanding the complex perceptions and attitudes that con- sumers hold toward vegan food and fashion. The insights gained from this research can aid in the design of more effective campaign strategies aimed at promoting vegan consumerism, ultimately contributing to a more widespread acceptance of ethical veganism in society.