• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1483-1498
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• 2022

While public interests in the metaverse are growing recently in the Korean media and research, its understanding has not been fully established yet. In this study, we aimed to probe whether the rapid growth in media attention about the metaverse has increased its usage as a buzzword accompanied by an absence of scientific context. We analyzed publications and online news containing "metaverse" from 2020 to 2022. The data analysis methods are 1) time series frequency, 2) keyword network, 3) natural language model. The findings indicate the perception gap about metaverse between research and news articles broadened as its popularity has grown. Research about metaverse gradually expanded its connections with related topics-virtual and augmented realities-focusing on social changes in a remote environment. However, media reporting frequently used "metaverse" as a buzzword rather than explaining its scientific background, stimulating the proliferation of related topics and the dispersion of news content. This study further discusses the need for a media strategy to improve public conception of the long-term development of the metaverse.

• Journal of KIISE
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• v.44 no.7
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• pp.742-747
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• 2017

In this paper, we propose a novel approach to improve the performance of the Convolutional Neural Network(CNN) word embedding model on top of word2vec with the result of performing like doc2vec in conducting a document classification task. The Word Piece Model(WPM) is empirically proven to outperform other tokenization methods such as the phrase unit, a part-of-speech tagger with substantial experimental evidence (classification rate: 79.5%). Further, we conducted an experiment to classify ten categories of news articles written in Korean by feeding words and document vectors generated by an application of WPM to the baseline and the proposed model. From the results of the experiment, we report the model we proposed showed a higher classification rate (89.88%) than its counterpart model (86.89%), achieving a 22.80% improvement. Throughout this research, it is demonstrated that applying doc2vec in the document classification task yields more effective results because doc2vec generates similar document vector representation for documents belonging to the same category.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.109-112
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• 2001

This paper proposes a method to classify different video contents using features of digital video. Classified video types are the news, drama, show, sports, and talk program. Features, such as intra-coded macroblock number St motion vector in P-picture in MPEG domain are used. The frame difference of YCbCr is also employed as a measure of classification. We detect the occurrences of cuts in a video for a measure of classification. Finally, back-propagation neural-network of 3 layers is used to classify video contents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.475-486
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• 2020

The purpose of present study is to identify the regional tourism resources using Webometric network analysis. The study focuses on Suseong area in Daegu metropolitan city. Various kinds of web-based data, for example, hit counts, online news, and public comments, were used to discover hot places and people's responses. The research question is, 'First, what is the optimum level of the search engine for suseong? Second, what is the online appearance of tourist resources in suseong? Which region is the center of tourism with high levels of emergence? Third, what are the main contents of news articles and comments related to the Suseong pond?'. The results show that the search engine optimization level in Suseong is lower than that in other areas in Daegu. In other words, tourism information and contents regarding Suseong are not highly visible on cyber space. Importantly, Suseong pond had the highest online presence. A close analysis of both online news and users' comments on Suseong pond, however, revealed the biggest concern as calling for improving public accessibility to tourism infrastructure. The findings are expected to contribute to policy development and service operation related to tourism resources in Suseong.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.183-191
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• 2020

The purpose of this study is to objectively analyze the trend of media articles related to the 'high school credit system' (2017~2019: 3 years), which has become the biggest concern among Korean education policies, through BIGKinds, a news data big data analysis service for media companies. The main research methodologies were BIGKinds system's specific search term news search, news trend analysis, keyword extraction and wordcloud implementation, network analysis and network picture presentation. The research results are as follows; First, the number of articles related to the high school credit system that appeared in major media outlets in Korea for 3 years from 2017 to 2019 was 3,649. The number of articles was sharply increased at a certain point about 4 times, based on the government's announcement of related policies. It showed an increasing news trend. Second, the top 20 keywords that emerged from the press articles related to the high school credit system for 3 years of analysis were presented, and it was confirmed that the keyword change by year appeared. Third, the network of media articles related to the high school credit system was visualized and presented in different ways by person, institution, and keyword. The results of this study confirmed that the high school credit system education policy was adopted as the representative education policy of the Moon Jae-in government, and is proceeding in the policy decision stage and policy implementation stage.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.1
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• pp.14-23
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• 2015

This study applied the network text analysis to reveal the scope and trends of low cost carrier studies. We analyzed low cost carrier research published in Korean journals and news articles. The results showed that there are three clusters in terms of research topics. First dimension consists of articles investigating growth in the low cost carrier industry. The second dimension is associated with service characteristics. The last dimension has strong ties organizational and human resource dimension. We run Krkwic, Krtitle, Netdraw, and Ucinet 6.0 to conduct the network text analysis. This study suggests the direction of low cost carrier research in the future.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.169-177
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• 2023

Network security is now more crucial than ever for consumers, companies, and military clients. Security has elevated to the top of the priority list since the Internet's creation. The evolution of security technology is now better understood. The area of community protection as a whole is broad and dynamic. News from the days before the internet and more recent advancements in community protection are both included in the topic of observation. Recognize current research techniques, previous Defence strategies that were significant, and network attack techniques that have been used before. The security of various domain names is the subject of this article's description of bibliographic research.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.1-10
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• 2021

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

• The Journal of Information Systems
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• v.31 no.1
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• pp.175-195
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• 2022

Purpose The purpose of this study is to examine news articles and analyze trends and key agendas related to artificial intelligence(AI). In particular, this study tried to compare the reporting behaviors of Korea and the United States, which is considered to be a leader in the field of AI. Design/methodology/approach This study analyzed news articles using a big data method. Specifically, main agendas of the two countries were derived and compared through the keyword frequency analysis, topic modeling, and language network analysis. Findings As a result of the keyword analysis, the introduction of AI and related services were reported importantly in Korea. In the US, the war of hegemony led by giant IT companies were widely covered in the media. The main topics in Korean media were 'Strategy in the 4th Industrial Revolution Era', 'Building a Digital Platform', 'Cultivating Future human resources', 'Building AI applications', 'Introduction of Chatbot Services', 'Launching AI Speaker', and 'Alphago Match'. The main topics of US media coverage were 'The Bright and Dark Sides of Future Technology', 'The War of Technology Hegemony', 'The Future of Mobility', 'AI and Daily Life', 'Social Media and Fake News', and 'The Emergence of Robots and the Future of Jobs'. The keywords with high centrality in Korea were 'release', 'service', 'base', 'robot', 'era', and 'Baduk or Go'. In the US, they were 'Google', 'Amazon', 'Facebook', 'China', 'Car', and 'Robot'.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.579-585
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• 2023

The purpose of this study is to explore keywords within stalking-related news articles according to political orientation through the text network analysis, and then to examine the implicit intentions. Selecting total 1,607 articles including 824 articles of the conservative press(The Chosun Ilbo, The Joongang Ilbo) and 783 articles of the progressive press(The Hankyoreh, The Kyunghyang Shinmun) reported from January 1, 2018 to December 31, 2022, this study explored the aspect of topic category drawn through the topic modeling technique based on LDA(Latent Dirichlet Allocation). In the results of this study, the common topics of the conservative and progressive press were improvement of the perception of gender-based violence, personal protection & intensity of punishment, and disclosure of stalkers' personal information. Regarding the topics differently shown in those two press, the conservative press showed stalkers' harmful act, and outline of 'murder case at Sindang Station' while the progressive press showed request for aggravated punishment on the 'murder case at Sindang Station', and eradication of sexual exploitation crime (in cyber space). The results of this study imply that there are changes in the type of reporting according to ideological opinions about stalking in news articles.