In this paper we discuss the use of clustering techniques to enhance the user experience and thus the success of collaborative tagging services. We show that clustering techniques can improve the user experience of current tagging services.

Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud’s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.

Abstract. TagClouds is a popular visualization for the collaborative tags. However it has some instinct problems such as linguistic issues, high semantic density and poor understanding of hierarchical structure and semantic relation between tags. In this paper we investigate the ways to support semantic understanding of collaborative tags and propose an improved visualization named TagClusters. Based on the semantic analysis of the collaborative tags in Last.fm, the semantic similar tags are clustered into different groups and the visual distance represents the semantic similarity between tags, and thus the visualization offers a better semantic understanding of collaborative tags. A comparative evaluation is conducted with TagClouds and TagClusters based on the same tags collection. The results indicate that TagClusters has advantages in supporting efficient browsing, searching, impression formation and matching. In the future work, we will explore the possibilities of supporting tag recommendation and tag-based Music Retrieval based on TagClusters.

Social tagging on online portals has become a trend now. It has emerged as one of the best ways of associating metadata with web objects. With the increase in the kinds of web objects becoming available, collaborative tagging of such objects is also developing along new dimensions. This popularity has led to a vast literature on social tagging. In this survey paper, we would like to summarize different techniques employed to study various aspects of tagging. Broadly, we would discuss about properties of tag streams, tagging models, tag semantics, generating recommendations using tags, visualizations of tags, applications of tags and problems associated with tagging usage. We would discuss topics like why people tag, what influences the choice of tags, how to model the tagging process, kinds of tags, different power laws observed in tagging domain, how tags are created, how to choose the right tags for recommendation, etc. We conclude with thoughts on future work in the area.

Abstract. In this paper, we describe the design steps of extending LAOS, a five-layer framework for generic adaptive web learning authoring, by adding a social layer to capture (and adapt) information from 1) collaborative authoring (i.e. editing the content of other learners, describing the content using tags, rating the content, and commenting on the content, etc); and 2) authoring for collaboration (i.e., adding authors ’ activities, such as defining groups of authors, subscribing to other authors, etc). Moreover, the paper presents MOT 2.0, an adaptive E-learning 2.0 system, which is built on the proposed reference model, and finally, we report on our evaluations to validate the new Social Layer by comparing MOT 2.0 with its predecessor, MOT 1.0.

Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that consist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well.

We present TubeKit, a query-based YouTube crawling toolkit. This software is a collection of tools that allows one to build one’s own crawler that can crawl YouTube based on a set of seed queries and collect up to 17 different attributes. TubeKit assists in the phases of this process starting with database creation to finally giving access to the collected data with browsing and searching interfaces. We further demonstrate how we used this toolkit to collect elections related data from YouTube for nearly two years. Some analysis of the collected data relating to the elections is also given.

Fig. 1. The Tag River visualization depicting data regarding user’s listening habits from the Last.fm social networking site. Abstract—Tag River is a novel visualization that presents a detailed comparative overview between user content for a particular span of time. Simultaneously it provides a trend summarization of earlier or later time spans. The summarization is displayed using vertically-adjacent polygonal regions in which the area represents some facet of quantitative information. A series of animated tag clouds are used to describe more detailed content for each user, changing over time to provide an indication of the coherence of context between time segments. The concurrent representation of both multivariate and temporal data can be cycled though programmatically or navigated interactively, allowing the user to explore time spans via filtering or zooming. Changing the view to a new time span instantly updates the tag clouds. We use color and size to represent information associated with the tags, and these aspects are updated to reflect changes in information when a new time span is selected. To facilitate these updates, we introduce a 2D packing algorithm which satisfies specified aesthetic criteria and runs at real-time frame rates. This paper describes the visualization technique in detail and presents example visualizations using datasets from social media sites. Index Terms—Text visualization, text analytics, social media, social network data, information visualization. 1