This paper describes mechanisms for asynchronous collaboration in the context of information visualization, recasting visualizations as not just analytic tools, but social spaces. We contribute the design and implementation of sense.us, a web site supporting asynchronous collaboration across a variety of visualization types. The site supports view sharing, discussion, graphical annotation, and social navigation and includes novel interaction elements. We report the results of user studies of the system, observing emergent patterns of social data analysis, including cycles of observation and hypothesis, and the complementary roles of social navigation and data-driven exploration. Author Keywords Information visualization, asynchronous collaboration, social data analysis, communication-minded visualization, groupware, bookmarks, annotation ACM Classification Keywords H.5.2. UI, H.5.3 Group and Organization Interfaces

Adaptive navigation support is a specific group of technologies that support user navigation in hyperspace, by adapting to the goals, preferences and knowledge of the individual user. These technologies, originally developed in the field of adaptive hypermedia, are becoming increasingly important in several adaptive Web applications, ranging from Web-based adaptive hypermedia to adaptive virtual reality. This chapter provides a brief introduction to adaptive navigation support, reviews major adaptive navigation support technologies and mechanisms, and illustrates these with a range of examples.

Tagging systems allow users to interactively annotate a pool of shared resources using descriptive strings, which are called tags. Tags are used to guide users to interesting resources and help them build communities that share their expertise and resources. As tagging systems are gaining in popularity, they become more susceptible to tag spam: misleading tags that are generated in order to increase the visibility of some resources or simply to confuse users. Our goal is to understand this problem better. In particular, we are interested in answers to questions such as: How many malicious users can a tagging system tolerate before results significantly degrade? What types of tagging systems are more vulnerable to malicious attacks? What would be the effort and the impact of employing a trusted moderator to find bad postings? Can a system automatically protect itself from spam, for instance, by exploiting user tag patterns? In a quest for answers to these questions, we introduce a framework for modeling tagging systems and user tagging behavior. We also describe a method for ranking documents matching a tag based on taggers ’ reliability. Using our framework, we study the behavior of existing approaches under malicious attacks and the impact of a moderator and our ranking method. 1.

We examine the recent information visualization phenomenon known as tag clouds, which are an interesting combination of data visualization, web design element, and social marker. Using qualitative methods, we find evidence that those who use tag clouds do so primarily because they are perceived as having an inherently social or personal component, in that they suggest what a person or a group of people is doing or is interested in, and to some degree how that changes over time; they are visually dynamic and thus suggest activity; they are a compact alternative to a long list; they signal that a site has tags; and they are perceived as being fun, popular, and/or hip. The primary reasons people object to tag clouds are their visual aesthetics, their questionable usability, their popularity among certain design circles, and what is perceived as a bias towards popular ideas and the downgrading of alternative views.

Enterprise communication applications rely on automated reasoning about factors such as expertise for connecting people. Quantifying expertise is necessary for such applications because of the time constraints imposed by communications routing. This paper discusses the potential for collaborative tagging in the enterprise and how it enables the formation of social networks around tags or topics. These social networks are reflective of the interests and expertise of users contributing to the tag. The tagging activity of a user contributes to the expertise of the user and influences the expertise of other users. The paper proposes a ranking mechanism for expertise based on the tagging activity of users for both unstructured and structured tag spaces. The paper also briefly describes a communication platform that will incorporate the ranking mechanism.

Employee directories play a valuable role in helping people find others to collaborate with, solve a problem, or provide needed expertise. Serving this role successfully requires accurate and up-to-date user profiles, yet few users take the time to maintain them. In this paper, we present a system that enables users to tag other users with key words that are displayed on their profiles. We discuss how people-tagging is a form of social bookmarking that enables people to organize their contacts into groups, annotate them with terms supporting future recall, and search for people by topic area. In addition, we show that people-tagging has a valuable side benefit: it enables the community to collectively maintain each others ’ interest and expertise profiles. Our user studies suggest that people tag other people as a form of contact management and that the tags they have been given are accurate descriptions of their interests and expertise. Moreover, none of the people interviewed reported offensive or inappropriate tags. Based on our results, we believe that people tagging will become an important tool for relationship management in an organization. ACM Classification H5.3 [Information interfaces and presentation]:

Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user’s profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user’s current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.

We present Collabio, a social tagging game within an online social network that encourages friends to tag one another. Collabio’s approach of incentivizing members of the social network to generate information about each other produces personalizing information about its users. We report usage log analysis, survey data, and a rating exercise demonstrating that Collabio tags are accurate and augment information that could have been scraped online. Author Keywords Social computing, social tagging, human computation.

ABSTRACT. In this paper, we explore various search tasks that are supported by a social bookmarking service. These bookmarking services hold great potential to powerfully combine personal tagging of information sources with interactive browsing, resulting in better social navigation. While there has been considerable interest in social tagging systems in recent years, little is known about their actual usage. In this paper, we present the results of a field study of a social bookmarking service that has been deployed in a large enterprise. We present new qualitative and quantitative data on how a corporate social tagging system was used, through both event logs (click level analysis) and interviews. We observed three types of search activities: community browsing, personal search, and explicit search. Community browsing was the most frequently used, and confirms the value of the social aspects of the system. We conclude that social bookmarking services support various kinds of exploratory search, and provide better personal bookmark management and enhance social navigation.

This paper outlines our experiences with applying collaborative tagging in e-learning systems to supplement more traditional metadata gathering approaches. Over the last 10 years, the learning object paradigm has emerged in e-learning and has caused standards bodies to focus on creating metadata repositories based upon strict domain-free taxonomies. We argue that the social collection phenomena and flexible metadata standards are key in collecting the kinds of metadata required for adaptable online learning. This paper takes a broad look at tagging within elearning. It first looks at the implications for tagging within the domain through an analysis of tags students provided when classifying learning objects. Next, it looks at two case studies based on novel interfaces for applying tagging. These two systems emphasize tags being applied within learning content through the use of a highlighting metaphor.