• The Journal of Bigdata
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• v.1 no.1
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• pp.1-7
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• 2016

Social media such as twitter has been popular by the diffusion of internet, and thanks to the radical improvement of computational ability of computers big data analysis became possible. This research is regarding about smart watch which is receiving attention as post-smartphone technology. Among various types of smart watch, this research focuses on the recently released Samsung Galaxy Gear S2. The main purpose of the research is to analyze customer's actual twitter data that was produced before and after the release of the smart watch to the market. Through the analysis, this research provides practical marketing strategy guideline, and also the analysis framework used in this research can be a research framework for other area and product researches.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.731-738
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• 2013

We analyze public opinion via a sentiment analysis of tweets collected by using recent topic keywords extracted from newspaper articles. Newspaper articles collected within a certain period of time are clustered by using K-means algorithm and topic keywords for each cluster are extracted by using term frequency. A sentiment analyzer learned by a machine learning method can classify tweets according to their polarity values. We have an assumption that tweets collected by using these topic keywords deal with the same topics as the newspaper articles mentioned if the tweets and the newspapers are generated around the same time. and we tried to verify the validity of this assumption.

• Journal of Korean Library and Information Science Society
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• v.46 no.3
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• pp.225-253
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• 2015

This study investigated the interactions between archives and users based on content analysis of posts of Facebook and Twitter operated by archival institutions. It focused on posts in official Facebooks and Twitters of the U.S. and the U.K. national archives. The posts included 66 in Facebook and 670 in Twitter of the U.S. national archives, as well as 73 in Facebook and 84 in Twitter of the U.K. national archive. The analysis showed that information sharing of in-house collections and online resources, as well as information dissemination of events were the most common interaction types of the posts. 1 and 1 communication or information gathering such as questionnaire or vote rarely happened. In addition, the extent of users' responses was great on posts regarding information sharing of in-house collections. Providing information about people or events with timely manners motivated interests and participations of users. It is necessary to consider various types of interactions that facilitate user engagement. It is also important to make efforts to provide timely records in connection with exiting web resources and a variety of social media provided by archival institutions.

• Journal of Internet Computing and Services
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• v.13 no.6
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• pp.9-16
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• 2012

Social Network Service (SNS) such as Twitter and Facebook has explosively grown nationwide since iPhone was introduced to Korea in 2009. In addition, KakaoStory has recently opened and joined to the SNS market, and it has grown to one of the most popular SNS in the domestic market in a short period of time. Social Network Service supports not only the formation of relationship between SNS users in common interests but also various activities such as management of personal connections and the sharing of information or contents. These three types of SNS have several common functions of sharing and distributing various contents rooted on the personal relationship formed through SNS. As each SNS user has specific reasons for the use of each service, a survey was conducted targeting those who use all of Twitter, Facebook, and KakaoStory was drawn by the statistical analyses of survey answers on users' reasons for each service. This result of study suggests factors to consider in order to exploit a new SNS or to enhance an existing service and can be used as a standard of which SNS for users to select for their own different purposes. It will also provide the basic data for the trust formation, one of the ethics in the upcoming Social Era.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1380-1386
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• 2015

Recently, the importance of users' geographic location information has been highlighted with a rapid increase of online social network services. In this paper, by utilizing geo-tagged tweets that provides high-precision location information of users, we first identify both Twitter users' exact location and the corresponding timestamp when the tweet was sent. Then, we analyze a relationship between the tweet frequency and the average user velocity. Specifically, we introduce a tweet-frequency computing algorithm, and show analysis results by country and by city. As a main result, it is shown that the tweet frequency according to user velocity follows a power-law distribution (i.e., Zipf' distribution or a Pareto distribution). In addition, by performing a comparison between the United States and Japan, one can see that the exponent of the distribution in Japan is smaller than that in the United States.

• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.492-497
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• 2014

In this paper, we propose a method of retrieving opinioned tweets in Twitter, which is the one of the popular Social Network Services and shares diverse opinions among various users. In typical opinion retrieval systems, they may consider the presence of sentiment phrases (subjectivity) as the important factor even if the subjective phrases are not related to a given query or speaker. To alleviate these problems, we utilized the syntactic structure of a sentence to identify the relationships between 1) subjectivity-query and 2) subjectivity-speaker and 3) the syntactic role of subjectivity. Besides, our learning-to-rank approach is trained to retrieve opinioned tweets based on query-relevance, textual features, user information, and Twitter-specific features. Experimental results on real world data show that our proposed method can achieve better performance than several baseline methods in terms of precision and nDCG.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.1
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• pp.83-108
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• 2019

This study investigated South Korean citizens' political communication and deliberation through Twitter during the 2014 General Election, focusing on influential opinion leaders' political information behaviors. Individual semi-structured interviews were administered with 13 opinion leaders using the multiple interview modes of email interviews, Skype interview and face to face interviews. Through the analytical lens of the theory of Information Worlds, the chosen social types and social norms of opinion leaders impacted their political information behaviors, including how they assessed, shared, exchanged, or avoided information, and, in turn, created boundaries between and around their information worlds that allowed for both conflict and synergy.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.389-390
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• 2017

현재 많은 사용자들이 모바일 기기를 통해 소셜 네트워크 서비스(이하 SNS)를 이용하고 있으며, SNS를 통해 수많은 데이터가 생성되고 있다. SNS상의 정보는 다양하고 신속하게 다루어지기 때문에 시대의 주요 사건을 잘 표현한다. 본 논문은 2015년 1월부터 2017년 8월까지의 약 191만개의 트위터 데이터를 수집한 후, LDA 모델링을 통해 주요 키워드를 추출하고 시대별 주요 토픽과 단어를 파악할 수 있는 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.849-852
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• 2017

본 논문에서는 SNS에서 수집한 데이터를 이용하여 사회 불안의 시공간 분포를 시각화 하는 기법을 소개한다. Open API인 twitter4j를 이용하여 트위터로부터 시공간 정보를 포함한 데이터를 수집한 뒤, 이 트윗의 작성자가 불안한지 아닌지 표시한 훈련 데이터를 준비한다. 이 훈련 데이터와 한글 형태소 분석기 Open API인 KOMORAN을 이용해 사전을 구축하고, 불안 분류기를 개발한다. 트위터로부터 수집한 시공간 정보를 포함한 데이터를 분류기로 분류하여, 지도에 표시해줌으로써 사회 불안을 시각화 한다. 사회 과학자들이 이를 이용하여 불안을 체계적으로 연구함으로써 불안으로부터 생기는 다양한 사회 문제들을 해결할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.871-872
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• 2013

최근 스마트폰의 급속한 보급에 따라 트위터, 페이스북과 같은 실시간 SNS(Social Network Service)가 폭발적으로 성장하고 있다. 본래 SNS는 지인과의 소통을 위한 온라인 커뮤니티로 출발했으나 지금은 새로운 커뮤니케이션으로 마케팅, 미디어, 커머스 등 다양한 영역의 플랫폼으로 진화하며, 그 파급력을 이어가고 있다. SNS, 소셜 미디어 시대를 맞이하여 소비자가 수동적 입장에서 능동적 입장으로 변경되고 있는 상황에서 블로그, 카페, 트위터 등 에서의 평가를 통한 고객 피드백 정보에 따라 서비스 제공자의 판매율이 많은 영향을 받고 있다. 따라서 효율적인 기업경영을 위해서는 SNS 등을 통한 고객의 목소리를 분석하는 작업과 그것을 기반으로 고객만족도 평가모형에 대한 연구가 필요하다. 본 논문에서는 이러한 SNS를 이용한 활용분야에 대해서 고찰해 보고 SNS가 점차 확대됨에 따라 발생할 수 있는 SNS 환경에서의 역기능은 어떤 것이 있는지 살펴보고 대응방안을 제안한다.