• Korean journal of communication and information
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• v.84
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• pp.152-188
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• 2017

This study stereotyped the media on the basis of ideological inclinations and media types and explored the news coverage through word analysis, network analysis, and frame analysis. There was no difference between conservative media and progressive media in terms of the amount of news. The conservative mainstream media considered the THAAD rumor as an unnecessary misunderstanding and a rumor based conflict of the south-south. The progressive mainstream media mentioned much about Hwang Gyoan, external influences, and lies and highlighted the government's opinion that there was external influence that spread a vicious rumor. Conservative media mentioned on the bringing about social disturbance and in case of progressive media mentioned social disturbance, and progressive media mentioned the responsibility of government and the attitude of conservative media about the diffusion of the rumor. In conclusion the press framed the THAAD rumor on the basis of their ideological inclinations instead of the role of journalist.

• Korean journal of communication and information
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• v.59
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• pp.31-49
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• 2012

This study investigates normative role model of the journalists under the changing environment. Firstly, this article explores what pressure the new media environment gives to the journalists in their routine of news production and distribution. These are stated from the angle of epistemological, professional and interactive pressure. Next, as a reference for the standard journalism in the age of mass media, the idea of Rhee Young-hee, a late journalist who won respects from many Korean journalists, is studied. His firm belief in the pursuit of hard facts, rigorous investigative writing and expertism are spelt out. Then, this study explores how, in real term, this pressure changes the journalistic value, norm and practices in the newsroom. Ten of Koran journalists are interviewed in order to get their idea about the emerging journalistic standards under the digital environment. From this in-depth interviews, it is conclued that the pursuit of hard fact, investigative writing, expertism of Rhee Young-hee are, nonetheless the change of the media technology, still effective and provide good reference points for the enhancement of the standard of journalism in Korea. However, it is also suggested that the methods to fulfil desirable journalism in the digital age should be different from that of the mass communication age. The interviewees make propose that the journalist, as a network node, news curator or coordinator, should actively interact with the audiences facilitating their enhanced potential as a news 'prosumer'(producer and consumer).

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.110-118
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• 2020

Social media data serves as a proxy indicator to understand the problems and the future of public opinion in Korean society. This research used 109,015 news data from 2016 to 2018 to analyze the sensitivity of the elderly and employment in Korean society, and explored the possibility of expanding the labor force in Korean society, which is facing a cliff between the elderly and the population. Topic keywords for employment of the elderly include "elderly*employment", "elderly*employment", and "elderly*wage". As a result of the analysis, positive sensitivity prevails for most of the period, and it is possible to expand the working-age population. Positive feelings about expanding employment opportunities for the elderly and negative feelings about low wages have brought to light the reality of the elderly who are still poor despite their work. In this study, social big data was used to analyze the perceptions and sensibilities of Korean society related to the elderly and employment through hierarchical crowd analysis and related text mining analysis.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.285-302
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• 2013

This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public's negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.267-272
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• 2018

Today, SNS exists as the most influential medium. A variety of designs are being produced in the context of numerous SNS marketing designs and the increasing exposure of ads to SNS and portal sites. Recognizing the current status of sns advertising design, which is heating up more than a decade ago, we will study strategies to introduce an important part of education to be conducted in universities and the current way in which it is used and produced. Modern SNS marketing designs include promotional posters, card news, and banner ads, and are evolving into various forms and content that are combined with advertisements. In communicating or sharing information, the importance of real-time advertising product work and the saving of caustic costs are important. This requires a diversification of university education, as well as a change in quality and speed of production. When the graduates of SNS Design Production Education Development Plan and design work were carried out, they conducted a study on the education section where real-time work is possible to proceed to the industrial field. Through this research, we would like to consider the development of SNS marketing design personnel and the practical implications of training methods.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.453-459
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• 2022

A decrease in fertility rates causes problems such as decrease in the working-age population, and has a significant impact on national policies. Currently, the Republic of Korea has a conscription system that imposes military service on all men over the age of 18. However, the transition to the volunteer miliatry system is emerging as a social issue due to the decrease in the fertility rate. In this paper, news articles and comments searched for through the keyword ' volunteer miliatry system' were collected to analyze the social perception of the volunteer miliatry system from 2018, when the fertility rate dropped to less than 1. Some of the collected comments were labeled, and emotional levels were calculated through deep learning models. Through this study, we found that awareness of recruitment system conversion did not increase as the decrease in the fertility rate, and it was confirmed that people's interest is gradually increasing.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.4
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• pp.277-285
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• 2022

There has been significant increase in demand of launch opportunities from the small satellite sector that represents the new space era. Providing smallsat-dedicated launch service at an affordable price is a new business model many startup companies have pursued, which requires innovative solutions for cost reduction in combination of low cost components, volume production and optimized manufacturing. We set out a preceding study at KARI to develop a suite of critical and cost-cutting technologies in preparation for a two-staged small launch vehicle development, based on the liquid rocket engine technologies developed from the Nuri program in accordance with the 3^rd master plan for national space development. In this work, we introduce the concept of a two-staged small launch vehicle that aims to be innovative and cost competitive for small satellites, and describe mission design results including staging as well as overall vehicle configuration of the launch vehicle.

• Spatial Information Research
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• v.22 no.5
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• pp.43-52
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• 2014

Recently, Big Data is a buzzword in the creative economy generation. The organizations related to spatial information society focus on building the spatial big data systems. As spatial big data is a combination of spatial information and big data, the data visualization is essential in order to utilize them efficiently. One of the great methodologies for data visualization is infographics. Nationally, Chousn.com initiated the infographics news in 2010. Korean Administration Branches also recognized the importance of infographic and they adopted infographics for their briefings from 2013. Internationally, Visual.ly is leading company in the infographics market and they produced noticeable interactive infographics for Egypt Parliamentary Elections results. In the defense part, Guardian's datajournalism of Afghanistan war log was a good example of utilizing infographics. Throughout the research, five requirements are extracted. First source data should have precision and accuracy in terms of time and space manner. Second, infographics images have a compressibility. Third, the infographics is properly processed for military commanders. Fourth, sharing, openness and communication are essential for high quality infographic. Lastly, infographics should be an analytic tool for predicting future event based on the past data. Infographics is not a direct representation of data but an analytic tool for helping user's choice and decision in critical moments.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.