Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

Introduction. This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tri-partite graphs, pattern tracing and descriptive statistics. This study is one of the few studies to employ multivariate analysis in investigating dimensions of Web spaces based on social tagging data. Method. This study examines the post data collected from a set of library and information science related Websites bookmarked on Delicious.com using a Web crawler. Post data consist of the URL, usernames, tags and comments assigned by users of Delicious.com. The collected tag data were analysed based on multivariate methods, such as multidimensional scaling and structural equation modelling. Analysis. Collected data were first analysed using multidimensional scaling to explore initial relationships amongst the selected Websites. Then, confirmatory factor analysis based on structural equation modelling was employed to examine the hierarchical structure of the library & information science Web space. Results. Social tag data exhibit different dimensions in the Web space of the library and information science field. In addition, social tags confirmed the hierarchical structure of the field by showing significantly stronger relationships between the sites with similar characteristics. That is, the structure of the tagging data shows similar connections to those present in the real world. Conclusions. This study suggests a new statistical approach in social tagging and Web space analysis studies. Tag information can be used to explain the hierarchical structure of a certain domain. Methodologically, this study suggests that structural equation modelling can be a compelling method to explore hierarchal structures of nodes on the Web space

#MPLP: a Comparison of Domain Novice and Expert User-generated Tags in a Minimally Processed Digital Archive

The high costs of creating and maintaining digital archives precluded many archives from providing users with digital content or increasing the amount of digitized materials. Studies have shown users increasingly demand immediate online access to archival materials with detailed descriptions (access points). The adoption of minimal processing to digital archives limits the access points at the folder or series level rather than the item-level description users\u27 desire. User-generated content such as tags, could supplement the minimally processed metadata, though users are reluctant to trust or use unmediated tags. This dissertation project explores the potential for controlling/mediating the supplemental metadata from user-generated tags through inclusion of only expert domain user-generated tags. The study was designed to answer three research questions with two parts each: 1(a) What are the similarities and differences between tags generated by expert and novice users in a minimally processed digital archive?, 1(b) Are there differences between expert and novice users\u27 opinions of the tagging experience and tag creation considerations?, 2(a) In what ways do tags generated by expert and/or novice users in a minimally processed collection correspond with metadata in a traditionally processed digital archive?, 2(b) Does user knowledge affect the proportion of tags matching unselected metadata in a minimally processed digital archive?, 3(a) In what ways do tags generated by expert and/or novice users in a minimally processed collection correspond with existing users\u27 search terms in a digital archive?, and 3(b) Does user knowledge affect the proportion of tags matching query terms in a minimally processed digital archive? The dissertation project was a mixed-methods, quasi-experimental design focused on tag generation within a sample minimally processed digital archive. The study used a sample collection of fifteen documents and fifteen photographs. Sixty participants divided into two groups (novices and experts) based on assessed prior knowledge of the sample collection\u27s domain generated tags for fifteen documents and fifteen photographs (a minimum of one tag per object). Participants completed a pre-questionnaire identifying prior knowledge, and use of social tagging and archives. Additionally, participants provided their opinions regarding factors associated with tagging including the tagging experience and considerations while creating tags through structured and open-ended questions in a post-questionnaire. An open-coding analysis of the created tags developed a coding scheme of six major categories and six subcategories. Application of the coding scheme categorized all generated tags. Additional descriptive statistics summarized the number of tags created by each domain group (expert, novice) for all objects and divided by format (photograph, document). T-tests and Chi-square tests explored the associations (and associative strengths) between domain knowledge and the number of tags created or types of tags created for all objects and divided by format. The subsequent analysis compared the tags with the metadata from the existing collection not displayed within the sample collection participants used. Descriptive statistics summarized the proportion of tags matching unselected metadata and Chi-square tests analyzed the findings for associations with domain knowledge. Finally, the author extracted existing users\u27 query terms from one month of server-log data and compared the generated-tags and unselected metadata. Descriptive statistics summarized the proportion of tags and unselected metadata matching query terms, and Chi-square tests analyzed the findings for associations with domain knowledge. Based on the findings, the author discussed the theoretical and practical implications of including social tags within a minimally processed digital archive

SEMANTIC DATA CLOUDING OVER THE WEBS

Very often, for business or personal needs, users require to retrieve, in a very fast way, all the available relevant information about a focused target entity, in order to take decisions, organize business work, plan future actions. To answer this kind of \u201centity\u201d- driven user needs, a huge multiplicity of web resources is actually available, coming from the Social Web and related user-centered services (e.g., news publishing, social networks, microblogging systems), from the Semantic Web and related ontologies and knowledge repositories, and from the conventional Web of Documents. The Ph.D. thesis is devoted to define the notion of in-cloud and a semantic clouding approach for the construction of in-clouds that works over the Social Web, the Semantic Web, and the Web of Documents. in-clouds are built for a target entity of interest to organize all relevant web resources, modeled as web data items, into a graph, on the basis of their level of prominence and reciprocal closeness. Prominence captures the importance of a web resource within the in-cloud, by distinguishing, also in a visual way \u201ca la tagcloud\u201d, how much relevant web resources are with respect to the target entity. The level of closeness between web resources is evaluated using matching and clustering techniques, with the goal of determining how similar web resources are to each other and with respect to the target entity

Découverte et analyse des communautés implicites par une approche sémantique en ligne (l'outil WebTribe)

Avec l essor du Web 2.0 et des technologies collaboratives qui y sont rattachées,le Web est aujourd hui devenu une vaste plate-forme d échanges entre internautes.La majeure partie des sites Web sont actuellement soit dédiés aux interactionssociales de leurs utilisateurs, soit proposent des outils pour développer ces interactions.Nos travaux portent sur la compréhension de ces échanges, ainsi que desstructures communautaires qui en découlent, au moyen d une approche sémantique.Pour répondre aux besoins de compréhension propres aux analystes de siteWeb et autres gestionnaires de communautés, nous analysons ces structures communautairespour en extraire des caractéristiques essentielles comme leurs centresthématiques et contributeurs centraux. Notre analyse sémantique s appuie notammentsur des ontologies légères de référence pour définir plusieurs nouvelles métriques,comme la centralité sémantique temporelle et la probabilité de propagationsémantique. Nous employons une approche en ligne afin de suivre l activitéutilisateur en temps réel, au sein de notre outil d analyse communautaire Web-Tribe. Nous avons implémenté et testé nos méthodes sur des données extraites desystèmes réels de communication sociale sur le WebWith the rise of Web 2.0 and collaborative technologies that are attached to,the Web has now become a broad platform of exchanges between users. The majorityof websites is now dedicated to social interactions of their users, or offerstools to develop these interactions. Our work focuses on the understanding of theseexchanges, as well as emerging community structures arising, through a semanticapproach. To meet the needs of web analysts, we analyze these community structuresto identify their essential characteristics as their thematic centers and centralcontributors. Our semantic analysis is mainly based on reference light ontologiesto define several new metrics such as the temporal semantic centrality and thesemantic propagation probability. We employ an online approach to monitor useractivity in real time in our community analysis tool WebTribe. We have implementedand tested our methods on real data from social communication systemson the WebDIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Queer Browsing and the Library of Congress Subject Headings: Can user-generated tags enhance subject access to LGBTQ+ material?

Within the field of Library and Information science, the treatment of LGBTQ+ topics in the Library of Congress subject headings is an emerging concern. Although changes have been implemented to the subject headings throughout the years, the hierarchical, oppositional nature of the system undoubtedly promotes heteronormative ideals. Moreover, the focus on uniformity and literary warrant means that ‘niché’ or non-dominant LGBTQ+ topics are often limited or obscured. The concept of adding user-tags into library catalogues has been popularised as a possible alternative. Due to this, there are an emerging number of tools that allow user-tags to be implemented as an overlay feature. This study aims to provide a comparison of Library of Congress’ subject headings and user-tags created by LibraryThing web users. To achieve this, a small pool of LGBTQ+ identifying participants were asked to complete a survey-based task to determine which terms they would use when searching for LGBTQ+ material. The participant terms were then compared with the terms that emerged within the user-tags and subject headings. Within the user-tags and participant data there was a high frequency of terms relating to “queer” identity and underrepresented LGBTQ+ groups, which were absent from the subject headings. However, the data also showed that the Library of Congress subject headings matched more of the participant given terms relating to intersectional LGBTQ+ issues. Due to their oppositional strengths and weaknesses, the research concludes that implementing user-tags to library catalogues is likely to enhance subject access to LGBTQ+ material, as long as it is implemented alongside the subject headings and does not replace them. This research is considered an exploratory study and results cannot be generalised

Emergent Community Structure in Social Tagging Systems

A distributed classification paradigm known as collaborative tagging has been widely adopted in new Web applications designed to manage and share online resources. Users of these applications organize resources (Web pages, digital photographs, academic papers) by associating with them freely chosen text labels, or tags. Here we leverage the social aspects of collaborative tagging and introduce a notion of resource distance based on the collective tagging activity of users. We collect data from a popular system and perform experiments showing that our definition of distance can be used to build a weighted network of resources with a detectable community structure. We show that this community structure clearly exposes the semantic relations among resources. The communities of resources that we observe are a genuinely emergent feature, resulting from the uncoordinated activity of a large number of users, and their detection paves the way for mapping emergent semantics in social tagging systems.Comment: 14 pages, 8 figure

EMERGENT COMMUNITY STRUCTURE IN SOCIAL TAGGING SYSTEMS

A distributed classification paradigm known as collaborative tagging has been widely adopted in new Web applications designed to manage and share online resources. Users of these applications organize resources (Web pages, digital photographs, academic papers) by associating with them freely chosen text labels, or tags. Here we leverage the social aspects of collaborative tagging and introduce a notion of resource distance based on the collective tagging activity of users. We collect data from a popular system and perform experiments showing that our definition of distance can be used to build a weighted network of resources with a detectable community structure. We show that this community structure clearly exposes the semantic relations among resources. The communities of resources that we observe are a genuinely emergent feature, resulting from the uncoordinated activity of a large number of users, and their detection paves the way for mapping emergent semantics in social tagging systems.Folksonomy, collaborative tagging, emergent semantics, online communities, Web 2.0