• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.752-758
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• 2012

SNS(Social Networking Services) is oneline service that enable users to construct human network through their relation on web, such as following relation, friend relation, and etc. Recently, owing to the advent of digital devices (smart phone, tablet PC) which embedded GPS some applications which provide services with spatial relevance and social relevance have been released. Such an online service is called LBSNS. It is required to use spatial filtering so as to build the LBSNS system that enable users to subscribe information of interesting area. For spatial filtering, user and tweet attaches location information which divide into static property presenting fixed area and dynamic property presenting user's area changed along the moving user. In the case of using a location information including dynamic property, Continuous query occurred from the moving user causes the problem in server. In this paper, we propose spatial filtering algorithm using Virtual Grid for reducing frequency of query, and conclude that frequency of query on using Virtual Grid is 93% decreased than frequency of query on not using Virtual Grid.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.667-677
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• 2013

With the huge success of Social Network Services, studies on social network analysis to extract the current issues or to track the symptoms of epidemic disease are being carried out actively. On Twitter, tweets reflect people's reaction to an event and users' individual status well, so it is possible to detect an event regarding a tweet as a sensory value. Recently, social signals are used to detect the spread of illness like the flu as well as the occurrence of disaster event like an earthquake in early stages. In this paper, we set up a cold as a target event and regarded tweets as Cold Signals. To evaluate the reliability of Cold Signals, we analyzed correlations between weather factors and the cold index provided by Korea Meteorological Administration.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.477-487
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• 2016

In 1995, the filming location of the drama had been famous, and as a result it brings the effect of increasing tourists of that areas. After that, many local governments try to host the filming on their regions to be potential tourist attractions. With the same stream, Jeonju also has attempted to host International Film Festival and to set up Jeonju Film Commission and Jeonju Cinema Complex. However, although the city already has rich infrastructure facilities to make films, the city hardly tries to use the filming locations as tourist attractions. This study suggests four ways of using filming locations as tourist attractions to activate Jeonju economy and improve Jeonju's cultural image. We firstly collect social bigdata related with tourists of filming locations and tourist attractions in Jeonju from Twitter, which is the most representative SNS, and then perform frequency and trend analysis. We also investigate major factors of visits to tourist's attractions based on content analysis of tweet mentions.

• Journal of Korean Library and Information Science Society
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• v.46 no.1
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• pp.115-133
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• 2015

This study investigates the contents of twitters serviced by metropolitans and citizens' questions to propose improvements. Using content analysis as a research method, this study recorded and analyzed all the tweets of six metropolitans (Seoul, Busan, Daegu, Incheon, Daejeon, Gwangju) for three months. As the results, the frequency analysis of tweets revealed that Busan posted more tweets than other cities, and Seoul posted the highest number of tweet using URL link. The results of content analysis showed that the most frequently provided information from tweeters was about convenience of citizens living. Tweets using URL link were focused on information about citizen living, prize contest, and service announcement. Citizens had a request for information about their life and traffic. For public information service using tweeter in the future, this study provided several important suggestions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.7-12
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• 2014

It has good features for social networking with friends SNS is maintained. However, various enterprises, individuals invading the inconvenience spammers have exposure to a number of users to tweet spam. The study was conducted in the existing research on these spam tweets. However, the results showed a more accurate classification and detection is difficult because of the lack of precision and different causes. In this paper, we describe how to classify the characteristics of spammers, classification criteria. Also has a link rate and difference between followers and following, these features were present classification criteria for spammers account. This experiment was performed according to the criteria. Randomized trial of spam and non-spam accounts were selected and account type was conducted according to the criteria 68% of the link ratio of spam accounts. Followers / Following ratio was 27581.5. Non-spam accounts was 6.12%. Followers / Following ratio was 1.26.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1103-1110
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• 2013

This paper proposes a method of identifying ownership of personal information in Social Network Service. In detail, the proposed method automatically decides whether any location information mentioned in twitter indicates the publisher's residence area. Identifying ownership of personal information is necessary part of evaluating risk of opened personal information online. The proposed method uses a set of decision rules that considers 13 features that are lexicographic and syntactic characteristics of the tweet sentences. In an experiment using real twitter data, the proposed method shows better performance (f1-score: 0.876) than the conventional document classification models such as naive bayesian that uses n-gram as a feature set.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.453-459
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• 2017

Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). While previous studies on discovering area-of-interests (AOIs) were conducted mostly on the basis of density-based clustering methods with the collection of geo-tagged photos from LBSNs, we focus on estimating a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a density-based low-complexity two-phase method to estimate a POI boundary by finding a suitable radius reachable from the POI center. We estimate a boundary of the POI as the convex hull of selected geo-tags through our two-phase density-based estimation, where each phase proceeds with different sizes of radius increment. It is shown that our method outperforms the conventional density-based clustering method in terms of computational complexity.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.471-478
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• 2012

Various studies on sentiment classification of documents have been performed. Recently, they have been applied to twitter sentiment classification. However, they did not show good performances because they did not consider the characteristics of tweets such as tweet structure, emoticons, spelling errors, and newly-coined words. In this paper, we perform experiments on various input features (emoticon polarity, retweet polarity, author polarity, and replacement words) which affect twitter sentiment classification model based on machine-learning techniques. In the experiments with a sentiment classification model based on a support vector machine, we found that the emoticon polarity features and the author polarity features can contribute to improve the performance of a twitter sentiment classification model. Then, we found that the retweet polarity features and the replacement words features do not affect the performance of a twitter sentiment classification model contrary to our expectations.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.65-75
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• 2014

The main focus of this paper is to analyze trend of event informations in a variety of mass media by graphical visualization in axis of the time and location. Especially, continuity analysis based on user-generated social media can reflect the social impact of a certain event according to change time and location and their directional changes. To reveal the characteristics of continuous events, we survey the data set collected from news articles and tweets during two years. Based on case studies on 'disease' and 'leisure', we verify the effectiveness and usefulness of our proposed method. Even though some events occurred during same period, we showed directional changes which have high-impact in social media referred user interest's, compared with fact-based continuous visualization results.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1167-1173
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• 2015

The development of information and communication technology like SNS, blogs, and bulletin boards, was provided a variety of places where you can express your thoughts and comments and allowing Big Data to grow, many people reveal the opinion of the social issues in SNS such as Twitter. In this study, we would like to pre-built sentimental dictionary about social issues and conduct a sentimental analysis with structured dictionary, to gather opinions on social issues that are created on twitter. The data that I used is "bikini", "nakkomsu" including tweet. As the result of analysis, precision is 61% and F1- score is 74%. This study expect to suggest the standard of dictionary construction allowing you to classify positive/negative opinion on specific social issues.