• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1139-1142
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• 2013

SNS(Social Network Service)를 사용하는 인구가 급증함으로 인해 개인의 생각이나 많은 자료들을 SNS 공간을 통해 공유함으로써 많은 문제들도 같이 발생하고 있는 추세이다. 대부분의 SNS는 자신의 공간에 게재된 정보에 대한 접근권한 만을 설정할 수 있고 자신이 타인의 공간에 게재한 게시물에 대해서는 접근 권한 설정에 대한 자격을 부여하지 않는다. 이를 통해 원치 않은 사용자들에게까지 자신의 개인 정보가 노출되는데, 이는 SNS 안에서의 문제만이 아니라 2차적인 문제도 만들어 낼 수 있다. 따라서 본 논문에서는 SNS 환경에서의 프라이버시 보호를 위한 태그 정보 접근 방법을 제안한다. 본 제안사항은 사용자의 태그정보에 대한 접근권한 설정을 통해 원치 않는 사용자가 2차적인 문제를 발생하지 않도록 제어할 수 있다.

• Journal of the Korean Society for information Management
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• v.26 no.2
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• pp.127-147
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• 2009

The synonym issue is an inherent barrier in human-computer communication, and it is more challenging in a Web 2.0 application, especially in social tagging applications. In an effort to resolve the issue, the goal of this study is to test the feasibility of a Web 2.0 application as a potential source for synonyms. This study investigates a way of identifying similar tags from a popular collaborative tagging application, Delicious. Specifically, we propose an algorithm (FolkSim) for measuring the similarity of social tags from Delicious. We compared FolkSim to a cosine-based similarity method and observed that the top-ranked tags on the similar list generated by FolkSim tend to be among the best possible similar tags in given choices. Also, the lists appear to be relatively better than the ones created by CosSim. We also observed that tag folksonomy and similar list resemble each other to a certain degree so that it possibly serves as an alternative outcome, especially in case the FolkSim-based list is unavailable or infeasible.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.24-27
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• 2008

As shown in Wikipedia, tagging or cross-linking through major key-words improves the readability of documents. Recently, the Semantic Web rises the importance of social tagging as a key feature of the Web 2.0 and Tag Cloud has emerged as its crucial phenotype. In this paper we provides an efficient method of automated keyword tagging based on controlled term collection, where the computational complexity of O(mN) - if pattern matching algorithm is used - can be reduced to O(mlogN) - if Information Retrieval is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that IR-based tagging speeds up 5.6 times compared with fast pattern matching algorithm.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.47-57
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• 2009

Collaborative tagging systems allow users to attach tags to diverse sharable contents in social networks. These tags provide usefulness in reusing the contents for all community members as well as their creators. Three-dimensional data composed of users, items, and tags are used in the collaborative tag-based recommendation. They are generally more voluminous and sparse than two-dimensional data composed of users and items. Therefore, there are many difficulties in applying existing collaborative filtering methods directly to them. Latent factor models, which are also successful in the area of collaborative filtering recently, discover latent features(factors) for explaining observed values and solve problems based on the features. However, establishing the models require much time and efforts. In order to apply the latent factor models to three-dimensional collaborative filtering data, we have to overcome the difficulty of establishing them. This paper proposes various methods for determining preferences of users to items via establishing an intuitive model by assuming tags used for items as latent factors to users and items respectively. They are compared using real data for concluding desirable directions.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.31-36
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• 2010

최근 대두되고 있는 웹2.0의 특징은 일반 사용자들이 능동적으로 정보를 생산해내고 공유하는데 있다. 웹 2.0의 참여형 아키텍쳐를 구성하는 핵심요소로 인식되고 있는 폭소노미(Folksonomy)는 과거 택소노미(Taxonomy)와 같이 전문가에 의하여 구축되는 분류 체계가 아닌 사용자들이 협동적으로 태그(Tag)들을 만들고 관리하는 소셜 태깅(Social Tagging)에 의한 분류 시스템이다. 최근 이러한 폭소노미를 활용하여 이미지를 공유하고 검색하고자 하는 다양한 시도들이 진행되고 있다. 그러나 Flickr와 같은 태그 기반 이미지 공유 시스템에서는 태그의 문법적, 의미적 모호성과 이미지에 대한 태그들의 중요성 또는 상관관계를 고려하지 않아 태그 기반 검색 시 정확성 및 신뢰성을 보장할 수 없다. 이러한 문제를 해결하기 위해 폭소노미에 기반한 이미지 공유 데이터베이스에서 적합한 태그들을 태그 전달(Tag Propagation)하거나 확률 및 출현빈도에 기반하여 태그 랭킹을 수행하기 위한 연구들이 활발히 진행되고 있지만 여전히 만족할만한 성능을 보이지 못하고 있다. 본 논문에서는 이미지 공유 데이터베이스에서 유사한 이미지들로부터 이미지에 보다 적합한 태그들을 부여하기 위해서, WordNet을 활용하여 태그들 간의 의미관계에 기반한 효율적인 태그 랭킹 기법을 제안한다. 또한, 신뢰성 있는 태그 기반 검색을 위하여 제안한 태그 랭킹 기법이 현재 이미지 공유 시스템의 랭킹 결과보다 정확성을 높일 수 있음을 실험 예제를 통하여 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.554-556
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• 2022

Due to the recent surge in single-person households, solitary death is drawing attention as an important social problem. We herein develop an adaptive monitoring system using RFID to protect the socially disadvantaged who are exposed to the risk of solitary death. The developed system consists of a wearable RFID tag, an RFID reader attached to the residence, and a user monitoring App. The developed system measures the retention time of a user with a wearable tag in a place where a reader is attached. When the retention time exceeds an adaptively determined threshold, an emergency notification is sent to the caregiver. We verify the effectiveness of the designed system by implementing a prototype modeling a residence based on Arduino.

• Journal of Information Management
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• v.43 no.2
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• pp.133-150
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• 2012

This study showed the activation of public library services utilizing twitter. Top five American public library twitter's 1,373 tweets collected, analyzed by content types and examined applicability into public library services. Based on the results, it suggested that public library services can be activated by auto-tweeting informations within home page, re-tweeting of timely informations, generating HASH tag, using diverse social medias, active re-tweeting/replying, and utilizing twitter programs such as twit-bot. Finally, the study proposed that evaluations about twitter services such as satisfaction survey should be carried out.

• 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.

• Korean Journal of Social Welfare
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• v.39
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• pp.290-321
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• 1999

This Study has two research interests: First, to give a new perspective in searching for the identifying features of social welfare studies in Korea where social welfare is recognized as an independent discipline through an examination of german research trends in social policy, where social policy is not recognized as an independent discipline, but as a field of study. The reasons of non-recognition of social policy studies as an independent discipline in Germany are value problems, vagueness of research objects, and the position of social welfare in relation to another social sciences. Second, to show the trends of german studies in social policy from diverse disciplines, i. e. sociology, political science, law, history, pedagogics etc. and the common points in these studies. The results of this study are as follows. First, the common feature of german Studies on the social policy from diverse disciplins is above all the interest in the improvement of Lebenslage, i. e. conditions of life. Second, the value problems in social sciences are not solved till now, but the interests in the improvement of Lebenslage don't mean studies of social policy must handle with values. The interests in the applicability of social policy don't mean values must be improved in the studies either. Third, the vagueness of the objects can be found also in other social sciences and is not unique in social policy studies. Fourth, the studies, which focuses on the improvement of Lebenslage and can contribute to construct theories such as raising the effectiveness of state intervention must be recognized as studies of social policy, even though they are written by social scientists from other disciplines. This means the theories of social policy to pursue are connected with theories of middle range, i. e. with lower degree of abstraction.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1125-1133
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• 2014

Recently, there are many researches have been studying for analyzing user interests and emotions based on users profiles and diverse information from Social Network Services (SNSs) due to their popularities. However, most of traditional researches are focusing on their researches based on single resource such as text, image, hash tag, and more, in order to obtain what user emotions are. Hence, this paper propose a method for obtaining user emotional information by analyzing texts and images both from Instagram which is one of the well-known image based SNSs. In order to extract emotional information from given images, we firstly apply GRAB-CUT algorithm to retrieve objects from given images. These retrieved objects will be regenerated by their representative colors, and compared with emotional vocabulary table for extracting which vocabularies are the most appropriate for the given images. Afterward, we will extract emotional vocabularies from text information in the comments for the given images, based on frequencies of adjective words. Finally, we will measure WUP similarities between adjective words and emotional words which extracted from the previous step. We believe that it is possible to obtain more precise user emotional information if we analyzed images and texts both time.