• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.357-365
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• 2015

Folk medicine is traditionally passed down to cure disease, and adverse events (AEs) of folk medicine are any unfavorable and unintended discomforts temporally associated with the use of folk medicine. The aim of this study was to analyze AEs types and risks of folk medicine through the internet news articles. Included in this analysis are all articles on the topic of folk medicine and AE reported in the top 3 online news websites (NAVER, DAUM and NATE) determined by InternetTrend$^{TM}$(www.internettrend.co.kr). It was searched in the last five years (between 1 January 2009 and 28 February 2014). In total, 18 AEs articles of 973 news articles met our inclusion criteria. A total of 27 people were experienced AEs associated with use of folk medicine. Age was from 4 months to 76 years old, and it was occurred in both men and women. Folk medicine that caused AEs in twice or more was therapy that patient taking the dictamnus or aconitum of toxic herbal medicines, vinegar therapy of external use to topical skin, and cupping or bee sting therapy by practitioners. Death as a kind of serious AEs was 11 people, and 10 people were died after treatment by unqualified practitioner. Folk medicine that is popular and widely used in Korea is actively interacted with information on the internet, so it apt to misuse and abuse without guidance of health professionals. Aspects of health care system, we point out that the need for government and medical society establish not only correct health information plan and promotion of risk but also system as reporting and monitoring of AEs by folk medicine.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.257-280
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• 2022

In this study, we analyzed comments on news articles of representative companies of the three industries (i.e., semiconductor, secondary battery, and bio industries) that had been listed as national strategic technology projects of South Korea to identify public opinions towards them. In addition, we analyzed the relationship between changes in public opinion and stock price. 'Samsung Electronics' and 'SK Hynix' in the semiconductor industry, 'Samsung SDI' and 'LG Chem' in the secondary battery industry, and 'Samsung Biologics' and 'Celltrion' in the bio-industry were selected as the representative companies and 47,452 comments of news articles about the companies that had been published from January 1, 2020, to December 31, 2020, were collected from Naver News. The comments were grouped into positive, neutral, and negative emotions, and the dynamic topics of comments over time in each group were analyzed to identify the trends of public opinion in each industry. As a result, in the case of the semiconductor industry, investment, COVID-19 related issues, trust in large companies such as Samsung Electronics, and mention of the damage caused by changes in government policy were the topics. In the case of secondary battery industries, references to investment, battery, and corporate issues were the topics. In the case of bio-industries, references to investment, COVID-19 related issues, and corporate issues were the topics. Next, to understand whether the sentiment of the comments is related to the actual stock price, for each company, the changes in the stock price and the sentiment values of the comments were compared and analyzed using visual analytics. As a result, we found a clear relationship between the changes in the sentiment value of public opinion and the stock price through the similar patterns shown in the change graphs. This study analyzed comments on news articles that are highly related to stock price, identified changes in public opinion trends in the COVID-19 era, and provided objective feedback to government agencies' policymaking.

• Management & Information Systems Review
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• v.37 no.4
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• pp.41-62
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• 2018

As the volume of unstructured data increases through various social media, Internet news articles, and blogs, the importance of text analysis and the studies are increasing. Since text analysis is mostly performed on a specific domain or topic, the importance of constructing and applying a domain-specific dictionary has been increased. The quality of dictionary has a direct impact on the results of the unstructured data analysis and it is much more important since it present a perspective of analysis. In the literature, most studies on text analysis has emphasized the importance of dictionaries to acquire clean and high quality results. However, unfortunately, a rigorous verification of the effects of dictionaries has not been studied, even if it is already known as the most essential factor of text analysis. In this paper, we generate three dictionaries in various ways from 39,800 news articles and analyze and verify the effect each dictionary on the accuracy of document classification by defining the concept of Intrinsic Rate. 1) A batch construction method which is building a dictionary based on the frequency of terms in the entire documents 2) A method of extracting the terms by category and integrating the terms 3) A method of extracting the features according to each category and integrating them. We compared accuracy of three artificial neural network-based document classifiers to evaluate the quality of dictionaries. As a result of the experiment, the accuracy tend to increase when the "Intrinsic Rate" is high and we found the possibility to improve accuracy of document classification by increasing the intrinsic rate of the dictionary.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.26-37
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• 2021

This study conducted a big data analysis on news to identify the agenda of media literacy, which has been socially discussed, and on which relevant policy directions will be proposed. To this end 1,336 articles from January 1, 2019 to September 30, 2020 were collected and a topic modeling analysis was conducted according to four periods. Five topics for each period were derived through the analysis, and implications based on the results are as follows. First, the government should implement a nation-level systematic approach to media literacy education according to life cycle stages to generate economic and cultural value. Second, local communities and schools should provide systematic support and education guidance activities to ensure a sustainable ecosystem for media literacy and prevent an educational gap and loss in learning. Third, efforts should be made in various aspects to minimize the side effects resulting from constantly providing media literacy education; furthermore a culture of desirable media application should be established. Finally, a research environment for scientific research on media literacy, active exchange of experience and value obtained in the field, and long-term accumulation of research results should be encouraged to develop a robust knowledge exchange culture.

• Informatization Policy
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• v.28 no.4
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• pp.19-35
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• 2021

The purpose of this research is to investigate the attributes of the online world and to analyze their influence on democracy. The research focuses on the mayoral by-elections that were held in Seoul and Busan, South Korea, on April 4, 2021. The study demonstrates the characteristics of online spaces and the polarization of the online public through news articles and user comments from the Internet. The research includes topic modeling to measure the diversity of media reports, sentiment analysis to measure online public opinion, and interrupted time series analysis to understand how a particular event influences online attitudes. A combination of these methods is used to attempt to estimate the strength of political polarity in the online environment. The study shows diverse media reports by election region and candidate, where the online public repeatedly reveals high negative and low positive attitudes towards each candidate. Moreover, political polarity can differ based on the level of interest in an election. Although voters pay less attention to a by-election than a presidential election, there is a solid political polarity in the online world. Hence, the research recommends preparing measures to alleviate the polarization as politics requires significant online participation.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.460-469
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• 2020

This study analyzes online news articles and cafe articles on the '5-day Rotation Mask Distribution System', which is emerging as a recent issue due to the COVID-19 incident, to identify the mass media and social media agendas containing media and public reactions. This study figured out the difference between mass media and social media. For analysis, we collected 2,096 full text articles from Naver and 1,840 posts from Naver Cafe, and conducted word frequency analysis, word cloud, and LDA topic modeling analysis through data preprocessing and refinement. As a result of analysis, social media showed real-life topics such as 'family members' purchase', 'the postponement of school opening', ' mask usage', and 'mask purchase', reflecting the characteristics of personal media. Social media was found to play a role of exchanging personal opinions, emotions, and information rather than delivering information. With the application of the research method applied to this study, social issues can be publicized through various media analysis and used as a reference in the process of establishing a policy agenda that evolves into a government agenda.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.768-773
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• 2015

Emergence events are the cause of much economic damage. In order to minimize the damage that these events cause, it must be possible to predict what will happen in the future. Accordingly, many researchers have focused on real-time monitoring, detecting events, and investigating events. In addition, there have also been many studies on predictive analysis for forecasting of future trends. However, most studies provide future tendency per event without contextual compositive analysis. In this paper, we present a predictive visual analytics system using topic composition to provide future trends per event. We first extract abnormal topics from social media data to find interesting and unexpected events. We then search for similar emergence patterns in the past. Relevant topics in the past are provided by news media data. Finally, the user combines the relevant topics and a new context is created for contextual prediction. In a case study, we demonstrate our visual analytics system with two different cases and validate our system with possible predictive story lines.

• Journal of Information Science Theory and Practice
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• v.2 no.4
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• pp.31-48
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• 2014

The proliferation of many interactive Topic Detection and Tracking (iTDT) systems has motivated researchers to design systems that can track and detect news better. iTDT focuses on user interaction, user evaluation, and user interfaces. Recently, increasing effort has been devoted to user interfaces to improve TDT systems by investigating not just the user interaction aspect but also user and task oriented evaluation. This study investigates the combination of the bag of words and named entities approaches implemented in the iTDT interface, called Interactive Event Tracking (iEvent), including what TDT tasks these approaches facilitate. iEvent is composed of three components, which are Cluster View (CV), Document View (DV), and Term View (TV). User experiments have been carried out amongst journalists to compare three settings of iEvent: Setup 1 and Setup 2 (baseline setups), and Setup 3 (experimental setup). Setup 1 used bag of words and Setup 2 used named entities, while Setup 3 used a combination of bag of words and named entities. Journalists were asked to perform TDT tasks: Tracking and Detection. Findings revealed that the combination of bag of words and named entities approaches generally facilitated the journalists to perform well in the TDT tasks. This study has confirmed that the combination approach in iTDT is useful and enhanced the effectiveness of users' performance in performing the TDT tasks. It gives suggestions on the features with their approaches which facilitated the journalists in performing the TDT tasks.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.285-307
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• 2019

In 2011, the Korean Medical Association revised the name of schizophrenia to remove the social stigma for the sick. Although it has been about nine years since the revision of the terminology, no studies have quantitatively analyzed how much social awareness has changed. Thus, this study investigates the changes in social awareness of schizophrenia caused by the revision of the disease name by analyzing Naver news articles related to the disease. For text analysis, LDA topic modeling, TF-IDF, word co-occurrence, and sentiment analysis techniques were used. The results showed that social awareness of the disease was more negative after the revision of the terminology. In addition, social awareness of the former term among two terms used after the revision was more negative. In other words, the revision of the disease did not resolve the stigma.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.145-156
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• 2022

In recent years, as there is growing concern with ESG (Environmental, Social, and Governance), the strategic direction of business management is changing from maximizing shareholders wealth to maximizing stakeholders value. ESG is reshaping a corporation's supply chain management strategies. The purpose of this study is to explore the ESG challenges in supply chain management. As a result of network text analysis and topic modeling analysis on 3226 news articles, 'Suppliers', 'Sustainability', 'Shared Growth' 'Carbon Neutral', 'Safety and Health', 'Responsible Business Alliance', 'Supply Chain Due Diligence Law' were identified as the main issue. Since ESG initiatives in the supply chain are not limited to the efforts of individual firms, future research should focus on figuring out what difficulties and challenges exist in the diffusion of ESG practices along multi-tiered supply chains, and how to overcome them.