• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.1
• /
• pp.139-151
• /
• 2023

This study aims to gain insights into ChatGPT, which has recently received significant attention. The study utilized a mixed method involving case studies and news big data analysis. ChatGPT can be described as an optimized language model for dialogue. The question arises whether ChatGPT will replace Google search services, posing a potential threat to Google. It could hurt Google's advertising business, which is the foundation of its profits. With AI-based chatbots like ChatGPT likely to disrupt the web search industry, Google is establishing a new AI strategy. The study used the BIG KINDS service and analyzed 2,136 articles over six months, from August 23, 2022, to February 22, 2023. Thirty of these articles were written in 2022, while 2,106 have been reported recently as of February 22, 2023. Also, the study examined the contents of ChatGPT by utilizing literature research, news big data analysis, and use cases. Despite limitations such as the potential for false information, analyzing news big data and use cases suggests that ChatGPT is worth using.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.10
• /
• pp.73-81
• /
• 2017

In this paper, we propose an analysis framework to capture the trends of information security incidents and evaluate the security policy based on the incident analysis. We build a big data from news media collecting security incidents news and policy news, identify key trends in information security from this, and present an analytical method for evaluating policies from the point of view of incidents. In more specific, we propose a network-based analysis model that allows us to easily identify the trends of information security incidents and policy at a glance, and a cosine similarity measure to find important events from incidents and policy announcements.

• 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.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.4
• /
• pp.37-43
• /
• 2022

In this paper, we calculate the word frequency by date and region using news data related to COVID-19 distributed for about 8 months from December 2019 to July 2020, and visualized the correlation with the current state data of COVID-19 patients using the results. News data was collected from Big Kids, a news big data system operated by the Korea Press Promotion Foundation. The visualization system proposed in this paper shows the news frequency of the selected region compared to the overall region, the key keyword of the selected region, the region of the main keyword, and the date change of the selected region. Through this visualization, the main keywords and trends of COVID-19 confirmed and infected people can be identified for previous events.

• Journal of The Korean Association of Information Education
• /
• v.26 no.6
• /
• pp.543-552
• /
• 2022

This study tried to analyze the social perception of news coverage on inclusive education by using big data analysis techniques. News articles were collected according to the 5-year policy period for the development of special education, and news big data was analyzed. As a result, the frequency of media reports during the five-year policy period of special education development from 1998 in the first year to 2022 in the fifth year was steadily increased. During this period, the top topic words in news coverage changed from words conceptualizing simple definitions to words expressing the active will of students with disabilities for the actual right to education. In addition, as a result of emotional analysis of the overall keywords in the inclusive education news coverage, it was found that the positive word ratio was high. Through this study, it can be seen that interest in news coverage on inclusive education is increasing quantitatively in accordance with changes in special education policies, and the demand for inclusive education is being concreted in the direction of guaranteeing the actual right to education of students with disabilities.

• Journal of the Korean Society for information Management
• /
• v.35 no.3
• /
• pp.41-75
• /
• 2018

The purpose of this study is to analyze the characteristics of Korean media on the topic of archives & records management based on time-series analysis. In this study, from January, 1999 to June, 2018, 4,680 news articles on archives & records management topics were extracted from BigKinds. In order to examine the characteristics of the media coverage on the archives & records management topic, this study was analyzed to the difference of the press coverage by period, subject, and type of the media. In addition, this study was conducted word-frequency based content analysis and semantic network analysis to investigate the content characteristics of media on the subject. Based on these results, this study was analyzed to the differences of media coverage by period, subject, and type of media. As a result, the news in the field of records management showed that there was a difference in the amount of news coverage and news contents by period, subject, and type of media. The amount of news coverage began to increase after the Presidential Records Management Act was enacted in 2007, and the largest amount of news was reported in 2013. Daily newspapers and financial newspapers reported the largest amount of news. As a result of analyzing news reports, during the first 10 years after 1999, news topics were formed around the issues arising from the application and diffusion process of the concept of archives & records management. However, since the enactment of the Presidential Records Management Act, archives & records management has become a major factor in political and social issues, and a large amount of political and social news has been reported.

• 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.

• International Journal of Advanced Culture Technology
• /
• v.11 no.4
• /
• pp.378-384
• /
• 2023

In this study, we explore social awareness, interest, and acceptance of generative AI, including chatGPT, which has revolutionized web search, 30 years after web search was released. For this purpose, we performed a machine learning-based topic modeling analysis based on Korean news big data collected from November 30, 2022, when chatGPT was released, to August 31, 2023. As a result of our research, we have identified seven topics related to chatGPT and generative AI; (1)growth of the high-performance hardware market, (2)service contents using generative AI, (3)technology development competition, (4)human resource development, (5)instructions for use, (6)revitalizing the domestic ecosystem, (7)expectations and concerns. We also explored monthly frequency changes in topics to explore social interest related to chatGPT and Generative AI. Based on our exploration results, we discussed the high social interest and issues regarding generative AI. We expect that the results of this study can be used as a precursor to research that analyzes and predicts the diffusion of innovation in generative AI.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.4
• /
• pp.495-500
• /
• 2021

This paper is to identify the recent growing crisis from coronavirus infections-19, using domestic news big data. This paper analyzed media articles related to the crisis caused by COVID-19 using the Korea Press Foundation's news big data analysis system 'BIGKinds'. In this paper, a total of 54 media articles were extracted around the keywords 'Corona' and 'Crisis', after a period of about 10 months. We want to understand the correlation coefficient between the two keywords "Corona" and "Crisis" and to understand what kind of crisis the COVID-19 is facing for each representative category of economy, society, international and cultural. As the COVID-19 crisis is taking a heavy toll on the economy, society and any other categories, this research using big data is expected to be used as a basic data to overcome the crisis of COVID-19.

• The Journal of the Korea Contents Association
• /
• v.20 no.5
• /
• pp.457-466
• /
• 2020

The purpose of this study is to find out what the main agenda of social formation is and how it changes through the media by utilizing the news big data of COVID-19 which is spreading recently, and to suggest the direction of future reporting. In order to achieve the purpose of the research, 47,816 cases of news big data reported from December 31, 2019 to March 11, 2020 were divided into four periods based on the fourth stage of the crisis warning for infectious diseases, and a total of 20 topics were derived. Based on the results of the Topic Modeling analysis, this study proposed the following. First, it is necessary to refrain from provocative expressions such as "anxiety" and "fear" and use neutral and objective reporting terms. Second, more in-depth and contextual news production is required, breaking away from simple event news production. Third, it is necessary to prepare detailed crisis communication manuals for each situation related to infectious diseases. Fourth, we need reports that focus on citizens-led efforts to overcome the crisis. This research has the academic significance that it is the first paper to analyze news big data on COVID-19 using the Topic Modeling Analysis method, and the policy significance that can be used as the basis for developing national crisis communication policy.