Identifying experts and authoritative documents in social bookmarking systems

Social bookmarking systems allow people to create pointers to Web resources in a shared, Web-based environment. These services allow users to add free-text labels, or “tags”, to their bookmarks as a way to organize resources for later recall. Ease-of-use, low cognitive barriers, and a lack of controlled vocabulary have allowed social bookmaking systems to grow exponentially over time. However, these same characteristics also raise concerns. Tags lack the formality of traditional classificatory metadata and suffer from the same vocabulary problems as full-text search engines. It is unclear how many valuable resources are untagged or tagged with noisy, irrelevant tags. With few restrictions to entry, annotation spamming adds noise to public social bookmarking systems. Furthermore, many algorithms for discovering semantic relations among tags do not scale to the Web. Recognizing these problems, we develop a novel graph-based Expert and Authoritative Resource Location (EARL) algorithm to find the most authoritative documents and expert users on a given topic in a social bookmarking system. In EARL’s first phase, we reduce noise in a Delicious dataset by isolating a smaller sub-network of “candidate experts”, users whose tagging behavior shows potential domain and classification expertise. In the second phase, a HITS-based graph analysis is performed on the candidate experts’ data to rank the top experts and authoritative documents by topic. To identify topics of interest in Delicious, we develop a distributed method to find subsets of frequently co-occurring tags shared by many candidate experts. We evaluated EARL’s ability to locate authoritative resources and domain experts in Delicious by conducting two independent experiments. The first experiment relies on human judges’ n-point scale ratings of resources suggested by three graph-based algorithms and Google. The second experiment evaluated the proposed approach’s ability to identify classification expertise through human judges’ n-point scale ratings of classification terms versus expert-generated data

Generation of Classificatory Metadata for Web Resources using Social Tags

With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata generation process, especially for semantic metadata. This research aims to find a method to categorize web resources using social tags as metadata. In this research, social tagging systems are a mechanism to allow non-professional catalogers to participate in metadata generation. Because social tags are not from a controlled vocabulary, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research examines ways to deal with those issues to obtain a set of tags representing the resource from the tags provided by users.Two measurements that measure the importance of a tag are introduced. Annotation Dominance (AD) is a measurement of how much a tag term is agreed to by users. Another is Cross Resources Annotation Discrimination (CRAD), a measurement to discriminate tags in the collection. It is designed to remove tags that are used broadly or narrowly in the collection. Further, the study suggests a process to identify and to manage compound tags. The research aims to select important annotations (meta-terms) and remove meaningless ones (noise) from the tag set. This study, therefore, suggests two main measurements for getting a subset of tags with classification potential. To evaluate the proposed approach to find classificatory metadata candidates, we rely on users' relevance judgments comparing suggested tag terms and expert metadata terms. Human judges rate how relevant each term is on an n-point scale based on the relevance of each of the terms for the given resource

Searching with Tags: Do Tags Help Users Find Things?

This study examines the question of whether tags can be useful in the process of information retrieval. Participants searched a social bookmarking tool specialising in academic articles (CiteULike) and an online journal database (Pubmed). Participant actions were captured using screen capture software and they were asked to describe their search process. Users did make use of tags in their search process, as a guide to searching and as hyperlinks to potentially useful articles. However, users also made use of controlled vocabularies in the journal database to locate useful search terms and of links to related articles supplied by the database

The role of social tags in web resource discovery: an evaluation of user-generated keywords

Social tags are user generated metadata and play vital role in Information Retrieval (IR) of web resources. This study is an attempt to determine the similarities between social tags extracted from LibraryThing and Library of Congress Subject Headings (LCSH) for the titles chosen for study by adopting Cosine similarity method. The result shows that social tags and controlled vocabularies are not quite similar due to the free nature of social tags mostly assigned by users whereas controlled vocabularies are attributed by subject experts. In the context of information retrieval and text mining, the Cosine similarity is most commonly adopted method to evaluate the similarity of vectors as it provides an important measurement in terms of degree to know how similar two documents are likely to be in relation to their subject matter. The LibraryThing tags and LCSH are represented in vectors to measure Cosine similarity between them

Finding cultural heritage images through a Dual-Perspective Navigation Framework

With the increasing volume of digital images, improving techniques for image findability is receiving heightened attention. The cultural heritage sector, with its vast resource of images, has realized the value of social tags and started using tags in parallel with controlled vocabularies to increase the odds of users finding images of interest. The research presented in this paper develops the Dual-Perspective Navigation Framework (DPNF), which integrates controlled vocabularies and social tags to represent the aboutness of an item more comprehensively, in order that the information scent can be maximized to facilitate resource findability. DPNF utilizes the mechanisms of faceted browsing and tag-based navigation to offer a seamless interaction between experts’ subject headings and public tags during image search. In a controlled user study, participants effectively completed more exploratory tasks with the DPNF interface than with the tag-only interface. DPNF is more efficient than both single descriptor interfaces (subject heading-only and tag-only interfaces). Participants spent significantly less time, fewer interface interactions, and less back tracking to complete an exploratory task without an extra workload. In addition, participants were more satisfied with the DPNF interface than with the others. The findings of this study can assist interface designers struggling with what information is most helpful to users and facilitate searching tasks. It also maximizes end users’ chances of finding target images by engaging image information from two sources: the professionals’ description of items in a collection and the crowd's assignment of social tags

Why Video Game Genres Fail: A Classificatory Analysis

This paper explores the current affordances and limitations of video game genre from a library and information science perspective with an emphasis on classification theory. We identify and discuss various purposes of genre relating to video games, including identity, collocation and retrieval, commercial marketing, and educational instruction. Through the use of examples, we discuss the ways in which these purposes are supported by genre classification and conceptualization, and the implications for video games. Suggestions for improved conceptualizations such as family resemblances, prototype theory, faceted classification, and appeal factors for video game genres are considered, with discussions of strengths and weaknesses. This analysis helps inform potential future practical applications for describing video games at cultural heritage institutions such as libraries, museums, and archives, as well as furthering the understanding of video game genre and genre classification for game studies at large

Retrieval of LGBTQ+ Recreational Reading Material: A Comparative Study

Over the past few decades, a sizeable body of library and information science literature has pointed to the inadequacies of traditional cataloging and classification systems for describing material related to marginalized communities. At the same time, alternative metadata systems have proliferated in online environments and social tagging has become almost ubiquitous. Focused specifically on the retrieval of LGBTQ+-related recreational reading materials, this study used an online survey to assess the utility of traditional library systems in comparison with the utility of the user-moderated folksonomy employed in the Archive of Our Own (AO3) fanwork repository. Results indicated that respondents, who were generally comfortable in both the library and Archive environments, preferred using AO3 to access LGBTQ+ material and perceived the tagging system to be of greater value in search processes than typical subject access mechanisms. Several possible avenues for improving current systems emerge in the conclusion of the paper.Master of Science in Information Scienc

User-generated descriptions of individual images versus labels of groups 3 of images: A comparison using basic level theory

Although images are visual information sources with little or no text associated with them, users still tend to use text to describe images and formulate queries. This is because digital libraries and search engines provide mostly text query options and rely on text annotations for representation and retrieval of the semantic content of images. While the main focus of image research is on indexing and retrieval of individual images, the general topic of image browsing and indexing, and retrieval of groups of images has not been adequately investigated. Comparisons of descriptions of individual images as well as labels of groups of images supplied by users using cognitive models are scarce. This work fills this gap. Using the basic level theory as a framework, a comparison of the descriptions of individual images and labels assigned to groups of images by 180 participants in three studies found a marked difference in their level of abstraction. Results confirm assertions by previous researchers in LIS and other fields that groups of images are labeled using more superordinate level terms while individual image descriptions are mainly at the basic level. Implications for design of image browsing interfaces, taxonomies, thesauri, and similar tools are discussed

Moody Blues: The Social Web, Tagging, and Nontextual Discovery Tools for Music

A common thread in discussions about the Next Generation Catalog is that it should incorporate features beyond the mere textual, one-way presentation of data. At the same time, traditional textual description of music materials often prohibits effective use of the catalog by specialists and nonspecialists alike. Librarians at Bowling Green State University have developed the HueTunes project to explore already established connections between music, color, and emotion, and incorporate those connections into a nontextual discovery tool that could enhance interdisciplinary as well as specialist use of the catalog