Folke Mitzlaff

The social bookmark and publication management system bibsonomy

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.

Enhancing Social Interactions at Conferences

Abstract Conferator is a novel social conference system that provides the management of social interactions and context information in ubiquitous and social environments. Using RFID and social networking technology, Conferator provides the means for effective management of personal contacts and according conference information before, during and after a conference. We describe the system in detail, before we analyze and discuss results of a typical application of the Conferator system. Zusammenfassung Als ein neuartiges soziales Konferenzmanagementsystem ermöglicht der Conferator die einfache Verwaltung sozialer Beziehungen und Interaktionen sowie das Management von konferenzspezifischen Informationen sowohl vor, während als auch nach einer Konferenz. Basierend auf RFID Technik gekoppelt mit sozialen Netzen bietet der Conferator die Möglichkeit, einfach und effektiv persönliche Kontakte und Informationen wie etwa den Konferenzplan zu verwalten. Wir beschreiben das System und präsentieren Analyseergebnisse in einem typischen Konferenz-Anwendungsszenario.

Face-to-face contacts at a conference: dynamics of communities and roles

This paper focuses on the community analysis of conference participants using their face-to-face contacts, visited talks, and tracks in a social and ubiquitous conferencing scenario. We consider human face-to-face contacts and perform a dynamic analysis of the number of contacts and their lengths. On these dimensions, we specifically investigate user-interaction and community structure according to different special interest groups during a conference. Additionally, using the community information, we examine different roles and their characteristic elements. The analysis is grounded using real-world conference data capturing community information about participants and their face-to-face contacts. The analysis results indicate, that the face-to-face contacts show inherent community structure grounded using the special interest groups. Furthermore, we provide individual and community-level properties, traces of different behavioral patterns, and characteristic (role) profiles.

Community assessment using evidence networks

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes an approach for (relative) community assessment. We introduce a set of so-called evidence networks which are capturing typical interactions in social network applications. Thus, we are able to apply a rich set of implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented approach applying user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.

Semantics of User Interaction in Social Media

In ubiquitous and social web applications, there are different user traces, for example, produced explicitly by ”tweeting” via twitter or implicitly, when the corresponding activities are logged within the application’s internal databases and log files.

Ubicon and its applications for ubiquitous social computing

The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques, and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.

Ubicon: Observing Physical and Social Activities

The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects.

Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy

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. Our results exhibit different characteristics and. provide preparatory work for the inclusion of new (and less sparse) information into the process of optimizing community detection or user recommendation algorithms.

Challenges in Tag Recommendations for Collaborative Tagging Systems

Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags. Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system.

The social distributional hypothesis: a pragmatic proxy for homophily in online social networks

Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the Distributional Hypothesis states that words with similar distributional characteristics tend to be semantically related, i.e., words which occur in similar contexts are assumed to have a similar meaning. Considering users as (social) entities, their distributional characteristics can be observed by collecting interactions in social web applications. Accordingly, we state the social distributional hypothesis: we presume, that users with similar interaction characteristics tend to be related. We conduct a series of experiments on social interaction networks from Twitter, Flickr, and BibSonomy and investigate the relatedness concerning the interactions, their frequency, and the specific interaction characteristics. The results indicate interrelations between structurally similarity of interaction characteristics and semantic relatedness of users, supporting the social distributional hypothesis.