• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.311-320
• /
• 2023

This paper analyzes viewers' perception of investigative journalism before and after COVID-19, and examines the direction of investigative journalism using big data. Based on the previous research set as a social science model, the relationship between words related to big data TV current affairs programs and investigative journalism in this paper was investigated before and after the appearance of COVID-19. We visualized changes in viewers' perception of investigative journalism by analyzing text data obtained through the use of Textom, with TV current affairs programs and investigative journalism as keywords. Data was collected from 2017 to June 2022 and refined for analysis. We visualized connectivity centrality using Ucinet 6.0 and Netdraw, and clustered the number of keywords and their frequency using Concor analysis. Our study found a clear change in viewer perception before and after the pandemic. As an implication of this thesis, big data analysis was conducted with the investigative journalism as the main keyword, and the direction of the investigative journalism was presented based on the analysis. Furthermore, based on previous research, we suggest effective approaches for investigative journalism after the pandemic to better engage viewers.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.21-37
• /
• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Information Systems Review
• /
• v.20 no.2
• /
• pp.139-161
• /
• 2018

As Korea became one of the oldest countries in the world, successful aging emerged as an important issue to individuals as well as to society. This study aims to determine not only the Korean seniors' happiness and unhappiness factors but also the means to enhance their happiness and deal with unhappiness. We collected news articles related to the happiness and unhappiness of seniors with nine keywords based on Alderfer's ERG Theory. We then applied a topic modeling technique, Latent Dirichlet Allocation, to examine the main issues underlying the seniors' happiness and unhappiness. According to the analysis, we investigated the conditions of happiness and unhappiness by inspecting the topics based on each keyword. We also conducted a detailed analysis based on the main factors from topic modeling. We proposed specific ways to increase and overcome the happiness and unhappiness of seniors, respectively, in terms of government, corporate, family, and other social welfare organizations. This study indicates the major factors that affect the happiness and unhappiness of seniors. Specific methods to boost happiness and relief unhappiness are suggested from the additional analysis.

• 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.

• Journal of the Korean Society for information Management
• /
• v.38 no.3
• /
• pp.23-39
• /
• 2021

This study aims to explore research trends in Blockchain studies in South Korea using dynamic topic modeling and network analysis. To achieve this goal, we conducted the university & institute collaboration network analysis, the keyword co-occurrence network analysis, and times series topic analysis using dynamic topic modeling. Through the university & institute collaboration network analysis, we found major universities such as Soongsil University, Soonchunhyang University, Korea University, Korea Advanced Institute of Science and Technology (KAIST) and major institutes such as Ministry of National Defense, Korea Railroad Research Institute, Samil PricewaterhouseCoopers, Electronics and Telecommunications Research Institute that led collaborative research. Next, through the analysis of the keyword co-occurrence network, we found major research keywords including virtual assets (Cryptocurrency, Bitcoin, Ethereum, Virtual currency), blockchain technology (Distributed ledger, Distributed ledger technology), finance (Smart contract), and information security (Security, privacy, Personal information). Smart contracts showed the highest scores in all network centrality measures showing its importance in the field. Finally, through the time series topic analysis, we identified five major topics including blockchain technology, blockchain ecosystem, blockchain application 1 (trade, online voting, real estate), blockchain application 2 (food, tourism, distribution, media), and blockchain application 3 (economy, finance). Changes of topics were also investigated by exploring proportions of representative keywords for each topic. The study is the first of its kind to attempt to conduct university & institute collaboration networks analysis and dynamic topic modeling-based times series topic analysis for exploring research trends in Blockchain studies in South Korea. Our results can be used by government agencies, universities, and research institutes to develop effective strategies of promoting university & institutes collaboration and interdisciplinary research in the field.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.155-175
• /
• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.6
• /
• pp.646-661
• /
• 2020

Technological innovations and fast-growing new internet businesses are changing the paradigm of traditional business management, having various impacts on society. The development of internet technology is also increasing the adverse effects on technological innovation, and in particular, cybercrime related to computers continues to increase with each technological innovation. The purpose of this study is to construct a cybercrime business model (CBM) by using the business model canvas (BMC) theory for cybercrime in order to reduce cybercrime, and this model is applied and analyzed based on types of Korean cybercrimes. For this study, a systematic literature review was conducted to determine the components of cybercrime, and 60 relevant documents were classified through a keyword-based literature search. Besides, qualitative research in the classified literature has led to the derivation of cybercrime into 18 sub-blocks and nine building blocks. This study applies BMC theory to this derivation of cybercrime and builds the CBM through proper redefinition. Lastly, the developed CBM could be applied to cybercrime in Korea to help cyber incident-response staff understand cybercrimes analytically. This study contributes to the development of a new analysis framework that can reduce cybercrime.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.555-563
• /
• 2016

Science and technology contents (research papers, patents, reports) are the most common reference material for researchers involved in research and development in the fields of science and technology. Based on various search elements (title, abstract, keyword, year of publication, name of journal, name of author, publisher, etc.), many services are available for users to search science and technology contents and bibliographic information owned by libraries. Authority data on organization name can be useful as an element for author identification and as an element to search for results produced by specific organizations. However, organization name is not taken into account by current search services for domestic academic information and bibliographic records. This study analyzes organization name data contained in the metadata of science and technology contents, which are the basis of the establishment of authority data, and proposes a method and system based on string containment and exact string matching.

• Journal of Information Management
• /
• v.37 no.1
• /
• pp.83-103
• /
• 2006

This study analyzes the classification schemes and classified information resources of the directory services provided by major web portals to complement keyword-based retrieval. Specifically, this study intends to quantitatively analyze the topic categories, the information resources by subject, and the information resources classified by the topic categories of three directories, Yahoo, Naver, and Empas. The result of this analysis reveals some differences among directory services. Overall, these directories show different ratios of referred categories to original categories depending on the subject area, and the categories regarded as format-based show the highest proportion of referred categories. In terms of the total amount of classified information resources, Yahoo has the largest number of resources. The directories compared have different amounts of resources depending on the subject area. The quantitative analysis of resources classified by the specific category is performed on the class of 'News & Media'. The result reveals that Naver and Empas contain overly specified categories compared to Yahoo, as far as the number of information resources categorized is concerned. Comparing the depth of the categories assigned by the three directories to the same information resources, it is found that, on average, Yahoo assigns one-step further segmented divisions than the other two directories to the identical resources.

• Health Policy and Management
• /
• v.25 no.4
• /
• pp.264-276
• /
• 2015

Background: Healthy cities of Korea have engaged in various activities regarding the Korea Healthy Cities Partnership, and research activities on healthy cities is one of the important area. In the present context, due to the current policy to pursue Sustainable Development Goals locally and globally, it is essential to emphasize the importance of healthy city. Therefore, it is important to identify the research trend related to healthy city. The aim of this study was to find out research trend of healthy city studies from 1990 to 2014 by reviewing published papers and studies systematically. Based on the finding of the study, the necessary implications on future research directions of the healthy city are obtained. Methods: The area of this study is domestic journal (Korea), international journal, thesis, and research report focusing on healthy city from 1990 to 2014. The selection of data was performed using keyword is based on domestic and international database. The analysis criteria were divided into year of publication, type of study, subjects, study methods, and study area. Results: One hundred twenty papers were selected for the analysis. Papers related to the healthy city issue were published 4.8 times in an average in a year during that the period. However, the number of papers published increased dramatically in the recent 4 years. Of total, 28 papers (44.4%) focused on the healthy city policy and urban environmental improvement, 18 papers (28.6%) focused on health promotion, and the remaining were program centered. Most papers (71 out of 120) used quantitative study methods. Of total studies, studies have conducted in Jinju city (9), Wonju city (8), Changwon city (6), and Gangnam-gu (5), respectively, as a study area of healthy city. Conclusion: First, domestic healthy city researches has been gradually increasing every year, over the past 10 years which has heightened interest in healthy cities. Second, the expansion of the various areas of research is required in order to contribute to future sustainable healthy city. Third, in recent years, by taking advantage of a variety of research methods, conducting the qualitative and mixed method research is considered to be a desirable change.