• Journal of the Korean Society for Library and Information Science
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• v.45 no.1
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• pp.189-210
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• 2011

This study aims to explore how users are tagging in order to utilize a folksonomy and whether they understand the social and interactive aspects of tagging in three different folksonomic systems, Connotea (www.connotea.org), Delicious(http://delicious.com), and CiteULike(www.citeulike.org). The study uses internet questionnaires, qualitative diary studies, and follow-up interviews to understand twelve participants' tagging activities associated with folksonomic interactions. The flow charts developed from the twelve participants showed that tagging was a quite complex process, in which each tagging activity was interconnected, and a variety of folksonomic system features were employed. Three main tagging activities involved in the tagging processes have been identified: item selection, tag assignment, and tag searching and discovery. During the tag assignment, participants would describe their tagging motivations related to various types of tags. Their perception of the usefulness of types of tags was different when their purpose was for social sharing rather than personal information management. While tagging, participants recognized the social potential of a folksonomic system and used interactive aspects of tagging via various features of the folksonomic system. It is hoped that this empirical study will provide insight into theoretical and practical issues regarding users' perceptions and use of folksonomy in accessing, sharing, and navigating internet resources.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.434-437
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• 2015

스마트폰 사용자가 급격하게 증가함에 따라 BYOD(Bring Your Own Device)정책을 펼치는 회사들이 많아졌지만 스마트폰 악성코드로 인해 회사 네트워크 전체가 위험한 상황에 직면해있다. 현재 다양한 스마트폰 악성코드 탐지 기법들이 연구되고 있지만 대부분 단일 어플리케이션을 대상으로 탐지를 수행하고 네트워크 환경과는 독립적으로 탐지한다는 단점이 존재한다. 따라서 본 논문에서는 네트워크 환경을 고려한 Tagging 기반의 프로세스 행위 감시 에이전트를 제안한다.

• Child Health Nursing Research
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• v.15 no.1
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• pp.53-60
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• 2009

Purpose: The purpose of this study was to analyze metacommunicative episodes comprised of nurses' metacommunicative behavior and children's responses occurring in the dyads of nurse-child within the context of an inpatient pediatric unit. Methods: Twelve dyads of nurses and children were videotaped for four hours each day over a two-day period as they interacted with each other on the inpatient unit. The metacommunicative episodes were recorded from the videotapes. The metacommunicative behavior was categorized within Shin's metacommunicative behaviors. Results: The total number of episodes between the nurses and children included in this study was 242. The most frequently used metacommunicative behavior was 'reflection', followed by tagging, baby talk, approaching, mediating eye level, friendly demand, encouraging, description of acts, symbolization, turnabouts, touching, and mimic voice, respectively. The most common response to the nurses' metacommunicative behavior was agreeing, followed by tension release. Conclusion: Young children responded positively to metacommunicative behavior by the nurses. Understanding metacommunicative behavior may help nurses approach their young patients more easily and with confidence.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.3
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• pp.151-163
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• 2010

It is necessary, due to changes in the information environment, to investigate problems in existing information retrieval systems. Ontologies and social tagging, which are a relatively new means of information organization, enable exploratory discovery of information. These two connect a thought of a user with the thoughts of numerous other people on the Internet. With these connection chains through the interactions, users are foraging information actively and exploratively. Thus, the purpose of this study is, through qualitative research methods, to identify numerous discovery facilitators provided by ontologies and social tagging, and to create an exploratory discovery model based on them. The results show that there are three uppermost categories in which 5, 4 and 4 subcategories are enumerated respectively. The first category, 'Browsing and Monitoring,' has 5 sub categories: Noticing the Needs, Being Aware, Perceiving, Stopping, and Examining a Resource. The second category, Actively Participating, has 4 categories: Constructing Meaning, Social Bookmarking and Tagging, Sharing on Social Networking, Specifying the Original Needs. The third category, Actively Extends Thinking, also has 4 categories: Social Learning, Emerging Fortuitous Discovery, Creative Thinking, Enhancing Problem Solving Abilities. This model could contribute to the design of information systems, which enhance the ability of exploratory discovery.

• Journal of Information Science Theory and Practice
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• v.6 no.2
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• pp.46-61
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• 2018

The mobile social networking application Instagram is a well-known platform for sharing photos and videos. Since it is folksonomy-oriented, it provides the possibility for image indexing and knowledge representation through the assignment of hashtags to posted content. The purpose of this study is to analyze how Instagram users tag their pictures regarding different kinds of picture and hashtag categories. For such a content analysis, a distinction is made between Food, Pets, Selfies, Friends, Activity, Art, Fashion, Quotes (captioned photos), Landscape, and Architecture image categories as well as Content-relatedness (ofness, aboutness, and iconology), Emotiveness, Isness, Performativeness, Fakeness, "Insta"-Tags, and Sentences as hashtag categories. Altogether, 14,649 hashtags of 1,000 Instagram images were intellectually analyzed (100 pictures for each image category). Research questions are stated as follows: RQ1: Are there any differences in relative frequencies of hashtags in the picture categories? On average the number of hashtags per picture is 15. Lowest average values received the categories Selfie (average 10.9 tags per picture) and Friends (average 11.7 tags per picture); for highest, the categories Pet (average 18.6 tags), Fashion (average 17.6 tags), and Landscape (average 16.8 tags). RQ2: Given a picture category, what is the distribution of hashtag categories; and given a hashtag category, what is the distribution of picture categories? 60.20% of all hashtags were classified into the category Content-relatedness. Categories Emotiveness (about 4.38%) and Sentences (0.99%) were less often frequent. RQ3: Is there any association between image categories and hashtag categories? A statistically significant association between hashtag categories and image categories on Instagram exists, as a chi-square test of independence shows. This study enables a first broad overview on the tagging behavior of Instagram users and is not limited to a specific hashtag or picture motive, like previous studies.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.4
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• pp.241-264
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• 2009

The study proposed an electronic journal retrieval model to be used to improve the retrieval efficiency of e-journals. To do this, firstly, we reviewed the literature on users' information behavior and on ERM (Electronic Resource Management) systems. Secondly, we conducted an e-mail survey of 96 participants (professors and graduate students) to find out about their information behavior on how to access, use and evaluate electronic resources as well as scientific information. Thirdly, we administered case studies on two ERMSs, Ex Libris' Verde and Innovative's Millennium. The proposed model will be operated within ERM systems and it enables to the supply of both system- and user-based services by combining taxonomy-based ERM systems with tag folksonomy. The model is unique in that it includes not only the automatic tagging functions that can be performed using log files but also the tag management functions including grouping similar or related tags.

• Journal of Information Management
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• v.40 no.2
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• pp.71-94
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• 2009

Indexing images using traditional indexing methods like taxonomy is not always efficient because of its visual content. This study examined how to apply folksonomies to image retrieval. To do this, first, we developed a category model for image tags found in Flickr. The model includes five categories and seventeen subcategories. Second, in order to evaluate the usefulness of the model to represent the various image tags as well as to investigate the end-user tagging behavior, three researchers classified the sampled image tags(141 most popular tags, 105 tags on three individual tag clouds and 3,848 image tags assigned on 156 images) according to the model. Finally, based on the research results, we proposed three methods for efficient image retrieval: extending folksonomies by combining them with ontologies; improving image retrieval efficiency using visual content and folksonomies; and updating taxonomy using folksonomies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.1-19
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• 2021

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

• Journal of Distribution Science
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• v.20 no.8
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• pp.123-133
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• 2022

Purpose: As moment-based marketing messages (i.e., messages related to current moments or event), companies put significant investments to distribute TV advertising related to external moments in a retail environment. While the literature offers strong support for the value of distributions of context-based messaging to advertisers, less attention has been given to how to design those messages to effectively communicate across channels. This research adds a new dimension of analysis to the study of advertising context and its cross-channel effects on online consumer behavior. Research Design, Data and Methodology: A system-of-equations Tobit regression model was adopted using data collected from an advertising agency that consists of 1,223 TV ads aired during the Rio Olympics and NCAA, tagging from consumers, and a text analysis. Results: First, TV ads with high centrality of context lead to lower online search behavior and higher online social actions. Second, how brands can design messages more effectively was explored by using product information as a moderator that could improve the impact of context-based TV advertisements. Conclusions: Given that expenses in traditional channels are still one of the biggest channel management decisions, it is critical to understand how consumer engagement varies by design of context-based TV advertising.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.44 no.2
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• pp.141-147
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• 2008

Acoustic telemetry technique is one of useful tools to get behavioral information of the free-swimming fish. In this study, we conducted acoustic telemetry using coded acoustic transmitters to compare behavior characteristics between wild and cultured black seabream Acanthopagrus schlegeli, one of target species to promote resource in the marine ranching area. Two wild fish and five cultured fish were released in the marine ranching area after tagging surgically. Three of cultured fish were domesticated using the remote acoustic conditioning system for 3 weeks before being released. Two wild fish stayed at the released point for 2 hours and 9 days, respectively. One of wild fish was found about 10.8km away from the released point after 5 months. Two cultured fish stayed at the released point for 6 days and 75 days, respectively. One of acoustic conditioned fish stayed at the released point for 131 days and then was found about 10.1km away from the released point after 25 days. Others stayed at the released point during this study period(159 days).