An Extensible Open-Source Framework for Social Network Analysis

Abstract Online communities that form social networks became extremely important in many tasks related with information processing, presentation, navigation, especially in context of web-based information systems. Web-based information systems employing communities could benefit from the classical studies of human social interactions – social network analysis. In this paper, we present an extensible open-source JAVA-based framework for social network analysis which can be used either a a stand-alone application with its own GUI as well as a library within third-party software projects developed in JAVA. We provide not only a standalone desktop application, but the whole framework, which allows anyone to incorporate results of social networks analysis into their own project, which would possibly boost up its functionality, enhance the results etc.

Improving user stereotypes generation through Machine Learning techniques

Users of Digital libraries require more intelligent interaction functionality to satisfy their needs. In this perspective, the most important features are flexibility and capability of adapting these functionalities to specific users. However, the main problem of current systems is their inability to support different needs of individual users due both to their inability to identify those needs, and, more importantly, to insufficient mapping of those needs to the available resources/services. The approaches considered in this paper to tackle such problems concern the use of Machine Learning techniques to adapt the set of user stereotypes with the aim of modelling user interests and behaviour in order to provide the most suitable service. A purposely designed simulation scenario was exploited to show the applicability of the proposal

Modeling users for adaptive semantics visualizations

The automatic adaptation of information visualization systems to the requirements of users plays a key-role in today's research. Different approaches from both disciplines try to face this phenomenon. The modeling of user is an essential part of a user-centered adaptation of visualization. In this paper we introduce a new approach for modeling users especially for semantic visualization systems. The approach consists of a three dimensional model, where semantic data, user and visualization are set in relation in different abstraction layer

Social Tagging for Graphic Novels: A Content Analysis of Graphic Novel Collections in New Zealand Public Libraries

Research problem: The problem addressed in this research concerns the lack of metadata in public library catalogue records for graphic novels. Although social tagging by library users may help to mitigate this, what kinds of words users might apply as social tags cannot be known. Methodology: Content analysis was undertaken to examine what social tags were applied to catalogue records for graphic novels from the Wellington City Libraries and Christchurch City Libraries, New Zealand. Based on previous research findings, categories such as topic, character, genre and setting, among others, were used as a basis for the themes of the content analysis. Records were examined, and the tags were coded at face using these categories. Results: Although the amount of social tags in the records was extensive and provided depth of information, the tags seemed to fit into multiple categories. Topic, character, genre, tags related to awards and personal tags were the most frequently used, with foreign language terms also being common. Reflecting previous research, there was a high degree of polysemy, synonymy, hypernymy and heteronymny in the words used as tags, with the hypernymy providing an inherent structure to the tags. Implications: Results may reflect the understanding and engagement users have with the items they are reading and because of this may make social tagging useful for other libraries