We present the design of a new social bookmark manager, named GalViz, as part of the interface of the GiveA-Link system. Unlike the interfaces of traditional social tagging tools, which usually display information in a list view, GalViz visualizes tags, resources, social links, and social context in an interactive network, combined with the tag cloud. Evaluations through a scenario case study and log analysis provide evidence of the effectiveness of our design.
GiveALink.org , as a research-oriented social tagging platform, broadly examines several aspects of social tagging to foster the construction and applications of socially driven semantic annotation networks. Previous research includes the design of effective similarity relationships @cite_3 , social spam detection @cite_5 , and social tagging games as incentive for collecting high-quality annotations @cite_7 . Former work in GiveA -Link on exploratory navigation interfaces @cite_19 and bookmark management @cite_9 have significant influence on the new design presented in the paper.
@cite_7 @cite_9 @cite_3 @cite_19 @cite_5
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Researchers are exploring the use of folksonomies, such as in social bookmarking systems, to build implicit links between online resources. Users create and reinforce links between resources through applying a common tag to those resources. The effectiveness of using such community-driven annotation depends on user participation to provide the critical information. However, the participation of many users is motivated by selfish reasons. An effective way to encourage these users is to create useful or entertaining applications. We demo two such tools -- a browser extension for bookmark management and navigation and a game. Social bookmarking systems are becoming increasingly important data sources for bootstrapping and maintaining Semantic Web applications. Their emergent information structures have become known as folksonomies. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as community detection, navigation support, semantic search, user profiling and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures, which are derived from several established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity between tags and between resources and consider different methods to aggregate annotations across users. After comparing the ability of several tag similarity measures to predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory Project. We also investigate the issue of scalability. We find that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity. The visualization of results is a critical component in search engines, and the standard ranked list interface has been a consistently predominant model. The emergence of social media provides a new opportunity to investigate visualization techniques that expose socially derived links between objects to support their exploration. Here we introduce and evaluate network-based visualizations for facilitating the exploration of a Web knowledge space. We developed a force directed network interface to visualize the result sets provided by GiveALink.org, a social bookmarking site. The classifications and tags by users are aggregated to build a social similarity network between bookmarked resources. We administered a user study to evaluate the potential of leveraging such social links in an exploratory search task. During exploration, the similarity links are used to arrange the resources in a semantic layout. Users in our study prefer a hybrid interface combining a conventional ranked list and a two dimensional network map, allowing them to find the same amount of relevant information using fewer queries. This behavior is a direct result of the additional structural information present in the network visualization, which aids them in the exploration of the information space. The popularity of social bookmarking sites has made them prime targets for spammers. Many of these systems require an administrator's time and energy to manually filter or remove spam. Here we discuss the motivations of social spam, and present a study of automatic detection of spammers in a social tagging system. We identify and analyze six distinct features that address various properties of social spam, finding that each of these features provides for a helpful signal to discriminate spammers from legitimate users. These features are then used in various machine learning algorithms for classification, achieving over 98 accuracy in detecting social spammers with 2 false positives. These promising results provide a new baseline for future efforts on social spam. We make our dataset publicly available to the research community.