This paper explores the possibility of using data from social bookmarking services to measure the use of information by academic researchers. Social bookmarking data can be used to augment participative methods (e.g. interviews and surveys) and other, non-participative methods (e.g. citation analysis and transaction logs) to measure the use of scholarly information. We use BibSonomy, a free resource-sharing system, as a case study. Results show that published journal articles are by far the most popular type of source bookmarked, followed by conference proceedings and books. Commercial journal publisher platforms are the most popular type of information resource bookmarked, followed by websites, records in databases and digital repositories. Usage of open access information resources is low in comparison with toll access journals. In the case of open access repositories, there is a marked preference for the use of subject-based repositories over institutional repositories. The results are consistent with those observed in related studies based on surveys and citation analysis, confirming the possible use of bookmarking data in studies of information behaviour in academic settings. The main advantages of using social bookmarking data are that is an unobtrusive approach, it captures the reading habits of researchers who are not necessarily authors, and data are readily available. The main limitation is that a significant amount of human resources is required in cleaning and standardizing the data.

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.

Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente wie Browsing und Suche am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.

The ongoing spread of online social networking and sharing sites has reshaped the way how people interact with each other. Analyzing the relatedness of different users within the resulting large populations of these systems plays an important role for tasks like user recommendation or community detection. Algorithms in these fields typically face the problem that explicit user relationships (like friend lists) are often very sparse. Surprisingly, implicit evidences (like click logs) of user relations have hardly been considered to this end. Based on our long-time experience with running BibSonomy [4], we identify in this paper different evidence networks of user relationships in our system. We broadly classify each network based on whether the links are explicitly established by the users (e.g., friendship or group membership) or accrue implicitly in the running system (e.g., when user u copies an entry of user v). We systematically analyze structural properties of these networks and whether topological closeness (in terms of the length of shortest paths) coincides with semantic similarity between users.

In this demo we present BibSonomy, a social bookmark and publication sharing system.

The enormous increase of popularity and use of the WWW has led in the recent years to important changes in the ways people communicate. An interesting example of this fact is provided by the now very popular social annotation systems, through which users annotate resources (such as web pages or digital photographs) with text keywords dubbed tags. Understanding the rich emerging structures resulting from the uncoordinated actions of users calls for an interdisciplinary effort. In particular concepts borrowed from statistical physics, such as random walks, and the complex networks framework, can effectively contribute to the mathematical modeling of social annotation systems. Here we show that the process of social annotation can be seen as a collective but uncoordinated exploration of an underlying semantic space, pictured as a graph, through a series of random walks. This modeling framework reproduces several aspects, so far unexplained, of social annotation, among which the peculiar growth of the size of the vocabulary used by the community and its complex network structure that represents an externalization of semantic structures grounded in cognition and typically hard to access.

BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.

The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.

The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.

In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and we propose practical models that use friendship and group membership information (which is often not hidden) to infer sensitive attributes. The key novel idea is that in addition to friendship links, groups can be carriers of significant information. We show that on several well-known social media sites, we can easily and accurately recover the information of private-profile users. To the best of our knowledge, this is the first work that uses link-based and group-based classification to study privacy implications in social networks with mixed public and private user profiles.

Recent growth of social classification systems due to steadily increasing popularity has established a multitude of heterogeneous isolated, non-integrated, and non-interoperable tag spaces. Contrary to current research predominantly focusing on single folksonomies, we exploit cross-space similarities to improve a variety of tagging use cases beyond the limits of one folksonomy. This paper presents the results of practical studies concerning cross-space analysis of (co-)tag spaces of five well-established social classification services for tagging of bookmarks (del.icio.us, BibSonomy bookmarks), and publications (BibSonomy publications, CiteULike, Connotea). The studies are based on one month data sets of RSS recent feeds from the same time scope. We provide a profound motivation for cross-space tagging, and give insight into similarities and intersections of (top ranking) (co-)tag spaces as well as convergence aspects over time.

BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.

Authors of scientific article have numerous options to search for background material for their research projects. With our article, we want to show that the use of Social-Bookmarking-Services as part of the web 2.0 (O’Reilly, 2005)/library 2.0 (Danowski, 2006) technology is a useful supplement to conventional reference databases.

Collaborative tagging has recently attracted the attention of both industry and academia due to the popularity of content-sharing systems such as CiteULike, del.icio.us, and Flickr. These systems give users the opportunity to add data items and to attach their own metadata (or tags) to stored data. The result is an effective content management tool for individual users. Recent studies, however, suggest that, as tagging communities grow, the added content and the metadata become harder to manage due to an ease in content diversity. Thus, mechanisms that cope with increase of diversity are fundamental to improve the scalability and usability of collaborative tagging systems. This paper analyzes whether usage patterns can be harnessed to improve navigability in a growing knowledge space. To this end, it presents a characterization of two collaborative tagging communities that target scientific literature: CiteULike and Bibsonomy. We explore three main directions: First, we analyze the tagging activity distribution across the user population. Second, we define new metrics for similarity in user interest and use these metrics to uncover the structure of the tagging communities we study. The structure we uncover suggests a clear segmentation of interests into a large number of individuals with unique preferences and a core set of users with interspersed interests. Finally, we offer preliminary results that demonstrate that the interest-based structure of the tagging community can be used to facilitate content usage as communities scale.

A trial with academic social referencing software – also called social bookmarking software – has been carried out with members of the Research Group Systems- and Network Engineering (SNE) of the University of Amsterdam. The idea for a user trial started after Marten Hoekstra of the SNE group contacted the University Library of the University of Amsterdam for advice and recommendation on using one of the academic social referencing tools. The University Library recognized the value of the social bookmarking site Del.icio.us, but concluded that this was not optimal for academic work1. Three other academic social referencing software tools were identified and analysed. Possible advantages of these academic social referencing tools for academics are listed in the textbox below. In order to assess the potential value of Web 2.0 applications for library services, and specifically academic social referencing tools, a user trial was set up by the University library with the members of the SNE group. Pleiade Management and Consultancy was asked to document and report the feedback from the users during the trial. The user trial was supervised by Driek van Heesakkers of the University library and Marten Hoekstra of the SNE group.

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies, briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references, and discuss first steps towards emergent semantics.