Search (69 results, page 3 of 4)

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Abstract

Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others.

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Abstract

To enable and guide effective metadata creation it is essential to understand the structure and patterns of the activities of the community around the photographs, resources used, and scale and quality of the socially created metadata relative to the metadata and knowledge already encoded in existing knowledge organization systems. This article presents an analysis of Flickr member discussions around the photographs of the Library of Congress photostream in Flickr. The article also reports on an analysis of the intrinsic and relational quality of the photostream tags relative to two knowledge organization systems: the Thesaurus for Graphic Materials (TGM) and the Library of Congress Subject Headings (LCSH). Thirty seven percent of the original tag set and 15.3% of the preprocessed set (after the removal of tags with fewer than three characters and URLs) were invalid or misspelled terms. Nouns, named entity terms, and complex terms constituted approximately 77% of the preprocessed set. More than a half of the photostream tags were not found in the TGM and LCSH, and more than a quarter of those terms were regular nouns and noun phrases. This suggests that these terms could be complimentary to more traditional methods of indexing using controlled vocabularies.

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Abstract

Purpose - The object of this empirical research study is emotion, as depicted and aroused in videos. This paper seeks to answer the questions: Are users able to index such emotions consistently? Are the users' votes usable for emotional video retrieval? Design/methodology/approach - The authors worked with a controlled vocabulary for nine basic emotions (love, happiness, fun, surprise, desire, sadness, anger, disgust and fear), a slide control for adjusting the emotions' intensity, and the approach of broad folksonomies. Different users tagged the same videos. The test persons had the task of indexing the emotions of 20 videos (reprocessed clips from YouTube). The authors distinguished between emotions which were depicted in the video and those that were evoked in the user. Data were received from 776 participants and a total of 279,360 slide control values were analyzed. Findings - The consistency of the users' votes is very high; the tag distributions for the particular videos' emotions are stable. The final shape of the distributions will be reached by the tagging activities of only very few users (less than 100). By applying the approach of power tags it is possible to separate the pivotal emotions of every document - if indeed there is any feeling at all. Originality/value - This paper is one of the first steps in the new research area of emotional information retrieval (EmIR). To the authors' knowledge, it is the first research project into the collective indexing of emotions in videos.

Type

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Abstract

Purpose - This study aims to consider the value of user-assigned image tags by comparing the facets that are represented in image tags with those that are present in image queries to see if there is a similarity in the way that users describe and search for images. Design/methodology/approach - A sample dataset was created by downloading a selection of images and associated tags from Flickr, the online photo-sharing web site. The tags were categorised using image facets from Shatford's matrix, which has been widely used in previous research into image indexing and retrieval. The facets present in the image tags were then compared with the results of previous research into image queries. Findings - The results reveal that there are broad similarities between the facets present in image tags and queries, with people and objects being the most common facet, followed by location. However, the results also show that there are differences in the level of specificity between tags and queries, with image tags containing more generic terms and image queries consisting of more specific terms. The study concludes that users do describe and search for images using similar image facets, but that measures to close the gap between specific queries and generic tags would improve the value of user tags in indexing image collections. Originality/value - Research into tagging has tended to focus on textual resources with less research into non-textual documents. In particular, little research has been undertaken into how user tags compare to the terms used in search queries, particularly in the context of digital images.

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Abstract

This study employs the FRBR (Functional Requirements for Bibliographic Records) conceptual model to provide in-depth investigation on the characteristics of social tags by analyzing the bibliographic attributes of tags that are not limited to subject properties. FRBR describes four different levels of entities (i.e., Work, Expression, Manifestation, and Item), which provide a distinguishing understanding of each entity in the bibliographic universe. In this research, since the scope of data analysis focuses on tags assigned to web documents, consideration on Manifestation and Item has been excluded. Accordingly, only the attributes of Work and Expression entity were investigated in order to map the attributes of tags to attributes defined in those entities. The content analysis on tag attributes was conducted on a total of 113 web documents regarding 11 attribute categories defined by FRBR. The findings identified essential bibliographic attributes of tags and tagging behaviors by subject. The findings showed that concerning specific subject areas, taggers exhibited different tagging behaviors representing distinctive features and tendencies. These results have led to the conclusion that there should be an increased awareness of diverse user needs by subject in terms of the practical implications of metadata generation.

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Abstract

Social tagging and controlled indexing both facilitate access to information resources. Given the increasing popularity of social tagging and the limitations of controlled indexing (primarily cost and scalability), it is reasonable to investigate to what degree social tagging could substitute for controlled indexing. In this study, we compared CiteULike tags to Medical Subject Headings (MeSH) terms for 231,388 citations indexed in MEDLINE. In addition to descriptive analyses of the data sets, we present a paper-by-paper analysis of tags and MeSH terms: the number of common annotations, Jaccard similarity, and coverage ratio. In the analysis, we apply three increasingly progressive levels of text processing, ranging from normalization to stemming, to reduce the impact of lexical differences. Annotations of our corpus consisted of over 76,968 distinct tags and 21,129 distinct MeSH terms. The top 20 tags/MeSH terms showed little direct overlap. On a paper-by-paper basis, the number of common annotations ranged from 0.29 to 0.5 and the Jaccard similarity from 2.12% to 3.3% using increased levels of text processing. At most, 77,834 citations (33.6%) shared at least one annotation. Our results show that CiteULike tags and MeSH terms are quite distinct lexically, reflecting different viewpoints/processes between social tagging and controlled indexing.

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Abstract

In many digital libraries, visual objects are published and metadata attached to allow for search and retrieval. For visual objects in which people appear, names are often added to the metadata so that digital library users can search for people appearing in these objects. Although this seems straightforward, there are ethical implications of adding names to metadata for visual objects. This paper explores the impact of this action and discusses relevant ethical issues it raises. It asserts that an individual's right to privacy and control over personal information must be weighed against the benefit of the object to society and the professional ethic to authentically represent a resource through its metadata. Context and an understanding of the major ethical issues will inform the practical decision of whether to keep objects online and add metadata to them, but items should generally be published unless there are clear ethical violations or a community relationship is in jeopardy.

Content

Beitrag aus einem Themenheft zu den Proceedings of the 2nd Milwaukee Conference on Ethics in Information Organization, June 15-16, 2012, School of Information Studies, University of Wisconsin-Milwaukee. Hope A. Olson, Conference Chair. Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_39_2012_5_c.pdf.

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Abstract

Little is known about what factors influence users' continued use of social cataloging sites. This study therefore examines the impacts of key factors from theories of information systems (IS) success and sense of community (SOC) on users' continuance intention in the social cataloging context. Data collected from an online survey of 323 social cataloging users provide empirical support for the research model. The findings indicate that both information quality (IQ) and system quality (SQ) are significant predictors of satisfaction and SOC, which in turn lead to users' intentions to continue using these sites. In addition, SOC was found to affect continuance intention not only directly, but also indirectly through satisfaction. Theoretically, this study draws attention to a largely unexplored but essential area of research in the social cataloging literature and provides a fundamental basis to understand the determinants of continued social cataloging usage. From a managerial perspective, the findings suggest that social cataloging service providers should constantly focus their efforts on the quality control of their contents and system, and the enhancement of SOC among their users.

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Abstract

Purpose - This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems. Design/methodology/approach - A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user-centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system. Findings - The results show that among the proposed UPSC formulas in this paper, the (query-document)-graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems. Research limitations/implications - This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information. Originality/value - The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.

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Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.

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Abstract

Social tags have become an important tool for improving access to online resources, particularly non-text media. With the dramatic growth of user-generated content, the importance of tags is likely to grow. However, while tagging behavior is well studied, the relationship between tagging behavior and features of the media being tagged is not well understood. In this paper, we examine the relationship between tagging behavior and image type. Through a lab-based study with 51 subjects and an analysis of an online dataset of image tags, we show that there are significant differences in the number, order, and type of tags that users assign based on their past experience with an image, the type of image being tagged, and other image features. We present these results and discuss the significant implications this work has for tag-based search algorithms, tag recommendation systems, and other interface issues.

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Abstract

The internet in its Web 2.0 version has given an opportunity among users to be participative and the chance to enhance the existing system, which makes it dynamic and collaborative. The activity of social tagging among researchers to organize the digital resources is an interesting study among information professionals. The one way of organizing the resources for future retrieval through these user-generated terms makes an interesting analysis by comparing them with professionally created controlled vocabularies. Here in this study, an attempt has been made to compare Library of Congress Subject Headings (LCSH) terms with LibraryThing social tags. In this comparative analysis, the results show that social tags can be used to enhance the metadata for information retrieval. But still, the uncontrolled nature of social tags is a concern and creates uncertainty among researchers.

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Abstract

The purpose of this paper is to determine semantic types for tags in social tagging systems. In social tagging systems, the determination of the semantic type of tags plays an important role in tag classification, increasing the semantic information of tags and establishing mapping relations between tagged resources and a normed ontology. The research reported in this paper constructs the semantic type library that is needed based on the Unified Medical Language System (UMLS) and FrameNet and determines the semantic type of selected tags that have been pretreated via direct matching using the Semantic Navigator tool, the Semantic Type Word Sense Disambiguation (STWSD) tools in UMLS, and artificial matching. And finally, we verify the feasibility of the determination of semantic type for tags by empirical analysis.

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Abstract

In this article, we describe the results of an experiment designed to understand the effects of background information and social interaction on image tagging. The participants in the experiment were asked to tag 12 preselected images of Jewish cultural heritage. The users were partitioned into three groups: the first group saw only the images with no additional information whatsoever, the second group saw the images plus a short, descriptive title, and the third group saw the images, the titles, and the URL of the page in which the image appeared. In the first stage of the experiment, each user tagged the images without seeing the tags provided by the other users. In the second stage, the users saw the tags assigned by others and were encouraged to interact. Results show that after the social interaction phase, the tag sets converged and the popular tags became even more popular. Although in all cases the total number of assigned tags increased after the social interaction phase, the number of distinct tags decreased in most cases. When viewing the image only, in some cases the users were not able to correctly identify what they saw in some of the pictures, but they overcame the initial difficulties after interaction. We conclude from this experiment that social interaction may lead to convergence in tagging and that the wisdom of the crowds helps overcome the difficulties due to the lack of information.

Type

a