• Journal of Broadcast Engineering
• /
• v.22 no.4
• /
• pp.438-447
• /
• 2017

The purpose of this study is to understand the recognition of gambling industry through the semantic analysis of news data on lottery, sports betting, horse racing and casino that was reported between 1990 to 2015 in South Korea. This paper revealed the difference between journalists' intention and public's perception about news by analyzing the frequency and connectivity of news with framing and public's interest through semantic network analysis and explored the policy characteristics and innovation task. The result of analysis, news on lottery game mainly has been reported social issue related with win such as 'winning number', 'prize money', 'suspicion of manipulation' and etc. News on sports betting has been reported mandatory information related with business project and illegal site such as 'bidding', 'illegal site', 'sales target' and etc. News about horse racing has been reported the information about the business advertisement such as 'online race track' and 'promotion'. Lastly, casino related news has been reported 'major information' such as illegality', 'gambling place' and 'foreigner'. As a result of times series analysis, news about casino in the 1990s, news about lottery in the 2000s and news about horse racing in 2010s have been increased. Public's interest also has been moved to 'business scandal', 'winning game', 'citizens' campaign' and etc. Gambling related news has been classified by four types, 1. advertising publicity(horse racing), 2. mandatory information(sports betting), 3. social issue(public agenda, lottery), 4. major information(casino). We could get the insight that news can be formed a public agenda, when news is reported as a social issue with high frequency and public's interest like lottery related news.

• The Journal of the Korea Contents Association
• /
• v.20 no.6
• /
• pp.556-578
• /
• 2020

There are many different researches about how to analyze issues based on real-time news streams. But, there are few researches which analyze issues hierarchically from news articles and even a previous research of hierarchical issue analysis make clustering speed slower as the increment of news articles. In this paper, we propose a hierarchical and incremental clustering for semi real-time issue analysis on news articles. We trained siamese neural network based weighted cosine similarity model, applied this model to k-means algorithm which is used to make word clusters and converted news articles to document vectors by using these word clusters. Finally, we initialized an issue cluster tree from document vectors, updated this tree whenever news articles happen, and analyzed issues in semi real-time. Through the experiment and evaluation, we showed that up to about 0.26 performance has been improved in terms of NMI. Also, in terms of speed of incremental clustering, we also showed about 10 times faster than before.

• The Journal of the Korea Contents Association
• /
• v.16 no.5
• /
• pp.385-398
• /
• 2016

This research analyzed domestic newspapers' agenda setting trend and meaning construction process on the issue of marine environment by year. The research conducted a language network analysis and used R program and Netminer program to analyze four major daily newspapers' news coverages (Dong-A, Joongang, Hanhyoreh, and Kyunghyang) for the last ten years (2005-2014). The results show that the issue of marine environment in Korean media reports are signified in the economic context. For the last ten years, news reports are mainly focused on the 'development' issue of marine environment, without distinction of year. The core key words of the networks are "development", "plan", and "project." However, diverse strategies for 'preservation' are not covered in media reports as a major issue. The importance of effective preservation and reasonable development should be considered in a balanced way. Korean media coverages mainly concentrate on the development issue, and it has a strong influence on considering the marine environment area as an object of development. Future direction and implication of the press reports related to marine environment are discussed.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.211-218
• /
• 2023

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.

• International journal of advanced smart convergence
• /
• v.12 no.1
• /
• pp.164-172
• /
• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.5
• /
• pp.129-153
• /
• 2023

This study was conducted in the context of the Covid-19 pandemic by analyzing a large amount of press report frames regarding the Covid-19 vaccine which is of great public interest, in order to explore the role and direction of trusted media as core elements of crisis communication. The study period lasted for eight months beginning in November 2020 when the development of the Covid-19 vaccine was in progress until June 2021. Set-up as research subjects were the Chosun Ilbo, Joongang Ilbo, Dong-A Ilbo and Hankyoreh according to their public confidence rankings and number of readers.The analysis method used structured topic Modeling (STM) and semantic network analysis. As a result, based on a clear cluster of word structures and a central analysis value, a total of 64 relevant frames, 16 for each news company, were gathered. In the third phase a comparative analysis of the four news companies was carried out to verify the organizational degree of the frames and substantial differences.

• Journal of the Korean Society for information Management
• /
• v.36 no.3
• /
• pp.7-36
• /
• 2019

In this study, quantitative and content analysis was conducted on 37,818 news articles that were reported on the subject of 'library' for 29 years from 1990 to 2018 in order to analyze the tendency of media coverage about 'library'. First, the quantitative change in media coverage was analyzed according to the criteria by time, subject and media type. In addition, keyword frequency analysis and semantic network analysis were conducted to analyze the trends of the contents of the press and the frames inherent in the press. The results showed that the media showed a major interest in the library's informational, educational, and cultural functions, and the trend of the subject's interest was generally consistent with that of the library community, except for the issue of librarianship. Lastly, the main attitudes that the media take toward the reporting of library articles were the reporting and advertising functions.

• The Journal of the Korea Contents Association
• /
• v.20 no.6
• /
• pp.293-311
• /
• 2020

Recently, it is more important to study the reasons for using media than which media is selected. This paper investigates different countries as objects to conduct the survey. In accordance with the research results, in hypothesis 1, there is a positive "(+)" influence of its interestingness, informality, restlessness, news pursuit and convenience on satisfaction when college students in South Korea use net news. Taking Chinese college students as an example, there is a positive "(+)" influence of the using motivation of net news on news pursuit, habituation, interactivity, convenience and the satisfaction with net news. In hypothesis 2, the interestingness, informality, habituation and convenience of the using motivation of online news of college students in South Korea are reflected in the acceptance intention of online news, while for Chinese college students, the informality, habituation and convenience are reflected in the acceptance intention of online news. Finally, in hypothesis 3, there is a positive "(+)" influence of the satisfaction of online news on the acceptance level of online news. In addition, this research also considers that the PLS path coefficient of college students in South Korea and China is different, and the motivations and purposes for using net news by two countries are different due to the characteristics and cultures of news media in different countries, so the satisfaction is also different.

• Korean journal of communication and information
• /
• v.56
• /
• pp.48-70
• /
• 2011

Although many researchers propose that market-driven journalism is incurred by the worsening of financial situation as a result of intensifying competition in mass media industry, few studies investigated this claim with actual news data. This study analyzed the headline news of eight major newspapers and two terrestrial TV companies to find the weakening of 'orientating-journalism' function of Korean news media. The results revealed that the duplication rate of news items among ten news companies were decreasing, and the range of news subjects were broadened into diverse topics during last ten years. Therefore it seemed that the tendency of monopolization of a certain events or issues was weakening in news reporting. The financial situation of news companies is an important factor in explaining the change of news reporting. The companies with more worse financial situation have higher duplication rate of news topics along as the more amount of soft news items, leading to the gradual deterioration of their own voices in reporting. The rate of 'independent issue report' was also less than seven precent, thus their reporting is evaluated as having many limitations. In sum, the major newspapers and network broadcasting companies are still exerting strong influences in agenda-setting, but they(mostly newspapers) are suffering from the financial problems, resulting the deterioration of performing orientation journalism function. This study concluded with remarks about the role of major news media in current changing situation.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.197-216
• /
• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.