• Journal of KIISE
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• v.43 no.9
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• pp.1043-1051
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• 2016

Web based entity linking cannot be applied in tweet entity linking because twitter documents are shorter in comparison to web documents. Therefore, tweet entity linking uses the information of users or groups. However, data sparseness problem is occurred due to the users with the inadequate number of twitter experience data; in addition, a negative impact on the accuracy of the linking result for users is possible when using the information of unrelated groups. To solve the data sparseness problem, we consider three features including the meanings from single tweets, the users' own tweet set and the sets of other users' tweets. Furthermore, we improve the performance and the accuracy of the tweet entity linking by assigning a weight to the information of users with a high similarity. Through a comparative experiment using actual twitter data, we verify that the proposed tweet entity linking has higher performance and accuracy than existing methods, and has a correlation with solving the data sparseness problem and improved linking accuracy for use of information of high similarity users.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.131-143
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• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.398-398
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• 2022

최근 수돗물 공급과정에 있어 적수, 유충 발생 등 지역 단위의 수질문제로 국민의 직간접적인 피해가 발생된 바 있다. 수질문제 발생 시, 소셜네트워크서비스(SNS)에 게시되는 피해 관련 의견은 시공간적으로 빠르게 확산되며, 궁극적으로는 물공급과정 전체의 부정적 인식증가와 신뢰도 저하를 초래한다. 따라서, 물공급시스템에서의 수질사고 발생을 빠르게 인지하는 다양한 방법론의 적용을 통한 피해 최소화를 위한 노력이 반드시 필요하다. 일반적으로 수질사고는 다양한 항목의 실시간 계측기에서 획득되는 시계열자료의 변화양상을 통해 판단할 수 있으나, 이와 같은 방법론의 효율적 적용을 위해서는 선진계측인프라의 도입이 선행되어야 한다. 본 연구에서는 국내의 발달된 정보통신기술환경을 활용하여, 물공급네트워크 내 수질사고인지를 위한 SNS 별 웹크롤링 방법론을 제안하고, 적용결과를 분석하였다. 방법론의 구현에 앞서, 각종 SNS 별(트위터, 인스타그램, 블로그, 네이버 카페 등) 프로그래밍을 통한 웹크롤링 가능여부, 정보획득 기간 등을 확인하였으며, 과거 유사 수질사고 발생 시 영향력과 관련 게시글이 크게 나타난 네이버 카페와 트위터를 중심으로 웹 크롤링 절차를 제시하였다. 네이버 카페의 경우 대상급수구역 내의 시민들이 다수 참여하는 카페를 목록화하고, 지자체명과 핵심 키워드(수돗물, 유충, 적수) 조합을 활용한 웹크롤링을 수행하여, 관련 게시물 건수와 의미를 실시간으로 분석하는 절차를 마련하였다. 개발된 SNS 별 웹크롤링 방법론에 따라 과거 수질사고가 발생된 바 있는 2개 이상의 지자체에 대한 분석을 실시하였으며, SNS 별 결과에 있어 차이점을 확인하여 제시하였다. 향후 제안된 방법을 적용하여 시공간적 수질사고 정보의 전파 및 확산양상을 추가적으로 분석할수 있을 것으로 기대된다.

• Informatization Policy
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• v.23 no.4
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• pp.76-94
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• 2016

Recently in Korea, the importance of information security awareness has been receiving a growing attention. Attacks such as social engineering and ransomware are hard to be prevented because it cannot be solved by information security technology. Also, the profitability of information security industry has been decreasing for years. Therefore, many companies try to find a new growth-engine and an entry to the foreign market. The main purpose of this paper is to draw out some information security issues and to analyze them. Finally, this study identifies issues and suggests how to improve the situation in Korea. For this, topic modeling analysis has been used to find information security issues of each country. Moreover, the score of sentiment analysis has been used to compare them. The study is exploring and explaining what critical issues are and how to improve the situation based on the identified issues of the Korean information security industry. Also, this study is also demonstrating how text mining can be applied to the context of information security awareness. From a pragmatic perspective, the study has the implications for information security enterprises. This study is expected to provide a new and realistic method for analyzing domestic and foreign issues using the analysis of real data of the Twitter API.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.83-90
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• 2015

This paper examines the rising value of social networked journalism and analyzes the case of a social news site based on the theory of networked journalism. Social networked journalism allows the public to be involved in every aspect of journalism production through crowd-sourcing and interactivity. The networking effect with the public is driving journalism to transform into a more open, more networked and more responsive venue. "wikitree" is a social networking news service on which anybody can write news and disseminate it via Facebook and Twitter. It is operated as an open sourced program which incorporates "Google Translate" to automatically convert all its content, enabling any global citizen with an Internet access to contribute news production and share either their own creative contents or generated contents from other sources. Since its inception, "wikitree global" site has been expanding its coverage rapidly with access points arising from 160 countries. Analyzing its international coverage by country and by news category as well as by the unique visit numbers via SNS, the results of the case study imply that networking with the global public can enhance news traffic to the social news site as well as to specific news items. The results also suggest that the utilization of Twitter and Facebook in social networked journalism can break the boundary between local and global public by extending news-gathering ability while growing audience's interest in the site, and engender a feasible business model for a local online journalism.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.15-28
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• 2016

This paper proposes a system recommending spatial information what user wants with collecting and analyzing tweets around the user's location by using the GPS information acquired in mobile. This system has built an emotion dictionary and then derive the recommendation score of morphological analyzed tweets to provide not just simple information but recommendation through the emotion analysis information. The system also calculates distance between the recommended tweets and user's latitude-longitude coordinates and the results showed the close order. This paper evaluates the result of the emotion analysis in a total of 10 areas with two keyword 'Restaurants' and 'Performance.' In the result, the number of tweets containing the words positive or negative are 122 of the total 210. In addition, 65 tweets classified as positive or negative by analyzing emotions after a morphological analysis and only 46 tweets contained the meaning of the positive or negative actually. This result shows the system detected tweets containing the emotional element with recall of 38% and performed emotion analysis with precision of 71%.

• Journal of Public Diplomacy
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• v.3 no.1
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• pp.21-42
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• 2023

Objective: To understand the contents and effects of public diplomacy by leaders, this paper throws out two questions: "To draw cooperation and support of international society in the middle of national crises, with what content do leaders conduct public diplomacy?" and "Through what mechanism, does leaders' public diplomacy promote cooperation and support of other countries?" Methods: To answer the research questions, this paper conducts a single case study with Ukrainian President Volodymyr Zelensky's public diplomacy in the Russia-Ukraine war in 2022. More specifically, the author collects text from Zelesky's Twitter and analyzes it in a descriptive manner. Results: When Russia invaded Ukraine in 2022, President Zelensky proactively utilized his SNS to communicate with people in other countries and thereby achieved the desired effect of public diplomacy. He has been delivering messages of 'appreciation of cooperation and support', 'situation of Ukraine', and 'congratulations or condolences', which could augment support, credibility, and audience cost. Conclusion: The effects of public diplomacy led to enhanced cooperation by alleviating problems within international cooperation and support. However, in the case of Ukraine, the patron's dilemma and commitment problem lasted, from which conducting public diplomacy has not been proven to be enough. The result of this paper provides lessons for the public diplomacy of the Republic of Korea in times of national crises in the future. In this regard, leaders should make efforts to promote the effectiveness of public diplomacy even in times of peace.

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

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.125-126
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• 2011

소셜 네트워크 서비스(SNS: Social Network Service)는 사용자들 간의 정보공유 및 관계형성을 위한 Web 2.0 기반 기술로써, 관계를 통해 형성된 사용자들 간의 상호작용을 지원하는 서비스이다. SNS 기술은 개인들 간의 관계 뿐 아니라 공공부문의 서비스의 제공자인 정부부처 및 공공서비스 제공기관들과 사회구성원들 간의 관계형성을 위한 기술로써 각광받고 있다. 최근에는 모바일 기술의 발전에 힘입어 트위터, 페이스북과 같은 SNS 이용자가 빠르게 증가하고 있으며 그 활용범위 또한 개인 사용자들 간의 정보공유에서 기업과 고객, 공공과 국민의 관계까지 확장하고 있다. 본 논문에서는 SNS가 공공부문의 서비스 제공을 위한 수단으로 활용한 사례를 통해 요구되는 기술을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.153-154
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• 2015

최근 스마트 기기의 발달과 정보통신기술의 발전은 트위터, 페이스북, 인스타그램 등의 소셜네트워크(social network service) 상에서 유통되는 정보량이 폭발적 증가하고 있다. 이러한 변화는 데이터화가 가속화되고 있는 현대사회에서 데이터의 가치는 점점 높아질 것으로 예상되며, 데이터로부터 가치 있는 정보와 통찰력을 효과적으로 이끌어내는 기업이 경쟁력 확보를 위한 핵심가치가 되었다. 글로벌 리서치 기관들은 빅 데이터를 2011년 이래로 최근 가장 주목받는 신기술로 지목해오고 있다. 따라서 대부분의 산업에서 기업들은 빅 데이터의 적용을 통해 가치 창출을 위한 노력을 기하고 있다. 본 연구에서는 다음 커뮤니케이션의 빅 데이터 분석도구인 소셜 매트릭스를 활용하여 키워드 분석을 통해 안드로이드와 애플 키워드 의미를 분석하고자 한다. 또한, 분석결과를 바탕으로 이론적 실무적 시사점을 제시하고자 한다.