• Korean journal of communication and information
• /
• v.40
• /
• pp.249-286
• /
• 2007

This study examines the main characteristics and differences on people's page in selected four national dailies, such as Chosun Ilbo, Joongang Ilbo, Seoul Shinmum and Hankyoreh Shinmun. Both Chosun Ilbo and Joongang Ilbo are categorized as the conservatives; while Seoul Shinmum and Hankyoreh Shinmun as the progressive. This study has been done by applying methods of content analysis to reveal the differences in terms of people's occupations, types of reports, their philosophies, and standards of selecting people by the dailies for the people's pages. The results shows that the conservative newspapers are very similar to the progressive newspapers in terms of occupations and the types of reports, covering the people on people's pages. More specifically, both the conservative dailies and the progressive dailies report the people for whose jobs are related to both social and cultural works. However, the conservative newspapers have much more coverages than the progressive papers in terms of the publicity reporting on the people. The conservatives are also much more reports on the people who are eager to economic success than the progressive dailies; while the progressives papers have much coverages on those who are interesting in helping others than the conservatives papers. Finally, this study reveals that the conservative dailies mainly cover those who are in publicity activities and social elite groups, while the progressive newspapers are the social celebrities and persons of fame in society.

• Journal of the Economic Geographical Society of Korea
• /
• v.19 no.2
• /
• pp.189-208
• /
• 2016

This research aims to discuss the impact of on regional development as a place brand and glocal heritage. Based on understanding of the changes and influences of local heritages in the globalization era as a key component of place branding, this study explores how is used to develop a place branding strategy for Lijiang. The research methods are both a literature review and a field research related to Lijiang and its culture. Also, the resources from news, internet, and YouTube are used to analyze the impact of . The performance has been attracting tourists from both Chineses and foreigners and contributed to increase the economic profits of local tourism industry as one of the representative identities of Lijiang. Also, in the process of preservation and recreation of cultural heritages of Lijiang such as , the participation of local residents and on-going interactions between the residents and global tourists highly influence on a transition from place marketing to place branding. By applying local cultural heritages to place branding strategies, the regional values of Lijiang strengthen its place identity from a place of preserving a minority's heritage to that of flourishing cultural exchanges and hybridization from the world.

• Journal of Korea Entertainment Industry Association
• /
• v.14 no.3
• /
• pp.211-227
• /
• 2020

This study intends to analyze the storytelling strategy of EBS's YouTube channel , which has been creating a remarkable cultural phenomenon since April 2019, and to examine the potential for its transition to transmedia franchise. Based on Henry Jenkins'seven principles of transmedia storytelling, shows satisfactory relevance in all but 'multiplicity'out of the 10 capacities consisting of the principles. In particular, was found excellent in terms of core capacities such as 'spreadability', 'immersion', 'worldbuilding', 'continuity', 'drillability' and 'performance'. In addition, critical discourse analysis(CDA) in sociocultural practice dimension of the keywords of the related news articles has discovered that is strongly linked to the following five values: 'social integration', 'resistance against authoritarianism', 'self-dignity and reasonable individualism', 'gender neutrality' and 'ecologism', indicating the reason why the work has been able to resonate so extraordinarily with participants across all generations. By answering the two chosen research questions, this study has proved that has high potential to be successful in evolving into transmedia franchise, while keeping building a new realm of edutainment storytelling by cleverly exploiting EBS's unique identity as a public education broadcaster. is viewed as an exceptional property capable of advancing transmedia storytelling in the local market; thus, productive arguments and contemplation over its evolution in storytelling needs to continue so that it can maintain a long-lasting positive influence.

• (The) Research of the performance art and culture
• /
• no.43
• /
• pp.229-261
• /
• 2021

On the musical stage in 2019, there were many works depicting the Japanese colonial period. This is due to 2019 the timeliness of the March 1st Movement and the centennial of the establishment of the Provisional Government of the Republic of Korea. The way of remembering and commemorating historical facts reflects the power relationship between memory subjects and the time, namely the politics of memory. Until now, stage dramas dealing with the era of Japanese rule have focused on the commemoration of modern national and national defense, including feelings of misfortune and respect for patriots. This study analyzed the metaphor of the memorials emphasized to the audience in the commemorative musicals Shingheung Military School, Ku: Songs of the Goblin, and Watch which were performed in 2019, and looked at how to adjust memories and memorials. The above works highlight the narratives of ordinary people as well as those recorded against the backdrop of the Manchurian Independence Movement and Hongkou Park, expanding the object of the commemoration. Through this, active armed resistance efforts, self-reflection and reflection were highlighted. The case of Shingheung Military School revealed the earnestness of ordinary people who led the independence movement through the movement of central figures. Ku: Songs of the Goblin revises memories by reproducing forgotten objects and apologizing through time slip. Watch has strengthened the spectacles of facilities through documentary techniques such as photography, news reels, and newspaper articles, but it also reveals limitations limited to records. In the 3.1 Movement and the 100th anniversary of the establishment of the Provisional Government of the Republic of Korea, devices that actively reveal that the "people's movement" is connected to the present. To this end, the official records reflected the newly produced values and memories and devoted themselves to the daily lives and emotions of the crowd. In addition, both empirical consideration and calligraphy were utilized to increase reliability. These attempts are meaningful in that they have achieved the achievement of forming contemporary empathy.

• Journal of The Korean Association For Science Education
• /
• v.32 no.4
• /
• pp.775-788
• /
• 2012

This study was done at a scientific camp sponsored by Nara Women's University Secondary School, Japan. In this school, $10^{th}$ grade students from 4 East Asian countries: Korea, Japan, Taiwan, and Singapore, participated. We made a research on students' perceptions about nuclear energy. Sample populations include 77 students in total, with 12 Korean, 46 Japanese, 9 Taiwanese and 10 Singaporean students. Overall perceptions comparison about nuclear energy shows average values from the order of highest Korea, Taiwan, Singapore, and to lowest, Japan. We implemented a T-test to identify perception differences about nuclear energy, with one group that include 3 countries (Korea, Taiwan and Singapore) and another group that includes all the Japanese students. T-test results of perceptions about nuclear energy shows students from the 3 countries of Korea, Taiwan and Singapore having higher average than Japanese students. (p<.05). Korean average scores regarding overall perceptions about nuclear energy show as the highest in all 4 East Asian countries and also highest in all subcategories. On the contrary in Japan, they have lower and negative perceptions of nuclear energy. In spite of these facts, perceptions of Japanese students about nuclear energy seem lowest and negative mainly because of the recent Fukushima nuclear power plant disaster, caused by the tsunami and its subsequent damages and fears of radiation leaks, etc. This shows that negative information about future disasters and its resulting damages like the Chernobyl nuclear accident could influence more on people's risk perception than general information like nuclear energy-related technologies or the news that the plant is operating normally, etc. Even if the possibility of this kind of accident is very low, just one accident could bring abnormal risks to technology itself. This strong signal makes negative image and strengthens its perceptions to the people. This could bring a stigma about nuclear energy. This study shows that Government's policy about the highest priority for nuclear energy safety is most important. As long as such perception and decision are fixed, we found that it might not be easy to get changed again because they were already fortified and maintained.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.119-138
• /
• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.33-54
• /
• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.109-122
• /
• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.