• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.89-96
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• 2015

Location-based social media data have high potential to be used in various area such as big data, location based services and so on. In this study, we applied a series of analysis methodology to figure out how the important keywords in location-based social media are spatially distributed by analyzing text information. For this purpose, we collected tweet data with geo-tag in Gangnam district and its environs in Seoul for a month of August 2013. From this tweet data, principle keywords are extracted. Among these, keywords of three categories such as food, entertainment and work and study are selected and classified by category. The spatial clustering is conducted to the tweet data which contains keywords in each category. Clusters of each category are compared with buildings and benchmark POIs in the same position. As a result of comparison, clusters of food category showed high consistency with commercial areas of large scale. Clusters of entertainment category corresponded with theaters and sports complex. Clusters of work and study showed high consistency with areas where private institutes and office buildings are concentrated.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.107-130
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• 2012

In Twitter, a user can post a message below 140 characters on his/her account, and can also repost a message of other users who the user follows. The message posted by the user in turn can be seen and reposted by other users who follow the user, which is called Re-tweet (RT). While some messages spread widely, other messages have relatively less or no RT. What factors cause these quantity variances of RT originated from original messages? How can the messages become influential in online social networks? As an effort to answer the above questions, we focused on information vividness, message characteristics, and originator characteristics. In perspective of managerial implication, we expect that the findings of this paper will provide corporations with helpful insight on the Word-of-Mouth (WOM) effect for efficient and effective advertisements and communications when they send a message of new products or services through Social Network Services. In perspective of academic implication, we identify the effect of contents of a message on WOM, which has been dealt with by few social network researches.

• Korean journal of communication and information
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• v.58
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• pp.55-73
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• 2012

This study is critical research on the forming public opinion of SNS and participation behavior. Twitter is a typical SNS service, free school food and 10.26 re-and by-elections during the formation of public opinion on the impact have been investigated. Formation of public opinion about Twitter's analysis of the first research question, The case of free meals to support the Mayor Oh, rather than against Twitter were influential. 10.26 re-and by-elections Twitter in space, the Park' candidate than Na' candidate has formed a favorable opinion. Power twitterian and twitterian on the behavior of the second study involved analysis of the problem, For free meals, power twitterian were responsible for leading the public opinion. For 10.26 re-and by-elections, Power twitterian were more Park' candidates than Na' candidates. In addition, Park' candidates of twitterians were communicating more. Through traditional media in the process of forming public opinion was swayed by public opinion to the target people simply but SNS age people involved in the production and distribution of the issue and the issue has a leading role.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06d
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• pp.316-321
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• 2010

웹 2.0의 기술이 보급됨에 따라 소셜 네트워크 서비스에 대한 관심이 증가하였다. 국내에서는 싸이월드, 미투데이 등과 같은 서비스가 널리 사용되고 있으며 최근 급부상한 트위터는 여러 분야에서 관심을 받고 있다. 트위터는 팔로워나 트윗 등 활동 정도에 따라 랭킹 서비스가 제공되고 있지만 랭킹은 그들 사이의 관계를 세부적으로 나타내지 못한다. 본 논문에서는 트위터의 사용자들 사이에 존재하는 관계를 시각적으로 나타내는 도구에 대해 개발한다. 국내 사용자 중 팔로워의 랭킹에 따른 사용자를 이용하고, 시각화를 위해 생물학적 데이터를 네트워크로 나타내는 Cytocape 플랫폼을 사용한다. 사용자 간의 관계를 나타내는 네트워크를 통하여 온라인상에서 영향력 있는 사용자들의 관계를 나타내고 그들의 관계를 수치로 분석한다. 또한 복잡한 네트워크로부터 선택된 노드와 관련된 연결만을 추출하는 기능을 제공하여 온라인상의 관계를 상세하게 나타낸다.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.11
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• pp.1719-1724
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• 2013

Big data which requires a different approach from existing data processing methods, is unstructured data with a variety of features. The features mean the volume of data, the rate of change of the data, the data with a variety of features. Tweets of twitter in only Korea are more than 5 millions per day. So much cheaper data storage and analysis system due to the increasing demand for information, the value of research is increasing. In this paper, the technology required by the deformation characteristics of the data elements as a technology priority-based word-based recommendation algorithm is proposed.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.131-140
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• 2019

This study compares and analyzes the spatial distribution of people in two cities using location information in twitter data. The target cities were selected as Paris, a traditional tourist city, and Dubai, a tourist city that has recently attracted attention. The data was collected over 123 days in 2016 and 125 days in 2018. We compared the spatial distribution of two cities according to the two periods and residence status. In this study, we have found a hot place using a spatial statistical model called dart-shaped space division and estimated the urban area by reflecting the distribution of tweet population. And we visualized it as a CDF (cumulative distribution function) curve so that the distance between all the tweets' occurrence points and the city center point can be compared for different cities.

• Journal of the Korean Society for information Management
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• v.36 no.2
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• pp.133-152
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• 2019

This study aims to identify and categorize the content and public use patterns of social scientists' informal communication activities. Using Twitter data, we identified Korean 736 social scientists who participated in communication activities with the public, and analyzed 4,548 tweets that revealed their informal communication activities. This study is meaningful in that it explored informal communication between social scientists and the public, which was not previously revealed in scholarly communication, and identified the types of informal communication activities, communication media, and collaborative sectors in detail.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter 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. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.249-255
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• 2014

Recently, creation and sharing of information make progress actively through the SNS(Social Network Service) such as twitter, facebook and so on. It is necessary to extract the knowledge from aggregated information and data mining is one of the knowledge based approach. Especially, emotion analysis is a recent subdiscipline of text classification, which is concerned with massive collective intelligence from an opinion, policy, propensity and sentiment. In this paper, We propose the emotion prediction method, which extracts the significant key words and related key words from SNS paragraph, then predicts the emotion using these extracted emotion features.