Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing any underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional categorization problem with ontologies? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources.

In Web 2.0 services “prosumers “- producers and consumers- collaborate not only for the purpose of creating content, but to index these pieces of information as well. Folksonomies permit actors to describe documents with subject headings, “tags“, without regarding any rules. Apart from a lot of benefits folksonomies have many shortcomings (e.g., lack of precision). In order to solve some of the problems we propose interpreting tags as natural language terms. Accordingly, we can introduce methods of NLP to solve the tags ’ linguistic problems. Additionally we present criteria for tagged documents to create a ranking by relevance (tag distribution, collaboration and actor-based aspects). We would like to open the discussion concerning the following aspects: Which tag distributions seem to be characteristic for folksonomies and how can we use these distributions effectively in information retrieval? What are the problems of indexing by using tags, especially regarding indexing photos and videos? How may we use factors of collaborative indexing for relevance ranking? Information Indexing Using “Collective Intelligence ” in Web 2.0 Services In the early years of the World Wide Web only few experts were able to distribute information via this new medium. The majority of the people dealing with the WWW solely acted as passive users. Yet, since the

This article presents a characterization of user behavior on Flickr, a popular on-line photo sharing service that allows users to store, search, sort and share their photos. Based on a sub-set of photos being uploaded during a 10 day window, we track the interest of users in those photos over a period of 50 days. In particular we investigate the user behavior on temporal, social, and spatial dimensions. Results show that the users are able to discover new photos within hours after being uploaded and that 50 % of the photo views are generated within the first two days. The social networking behavior of users, and photo pooling are identified as the two major indicators related to a photo’s popularity. Finally we show that the geographic distribution is more focussed around a geographic location for the infrequently viewed photos, than for the photos that attract a large number of views. 1.

Folksonomies, collaboratively created sets of metadata, are becoming more and more important for organising information and knowledge of communites in the Web. While for a single user the difference to keyword assignment is marginal, the power of folksonomies emerges from the collaborative aspects. Folksonomies are already issue of research. Within this publication we analyse underlying statistical properties of broad folksonomies aiming to identify laws and characteristics, which allow inferring properties for folksonomy based retrieval. The actual benefit of folksonomies for retrieval and the derived methods are concluded from experiments with aggregated data from del.icio.us 1. 1

Social bookmarks allow Web users to actively annotate individual Web resources. Researchers are exploring the use of these annotations to create implicit links between online resources. We define an implicit link as a relationship between two online resources established by the Web community. An individual may create or reinforce a relationship between two resources by applying a common tag or organizing them in a common folder. This has led to the exploration of techniques for building networks of resources, categories, and people using the social annotations. In order for these techniques to move from the lab to the real world, efficient building and maintenance of these potentially large networks remains a major obstacle. Methods for assembling and indexing these large networks will allow researchers to run more rigorous assessments of their proposed techniques. Toward this goal we explore an approach from the sparse matrix literature and apply it to our system, GiveALink.org. We also investigate distributing the assembly, allowing us to grow the network with the body of resources, annotations, and users. Dividing the network is effective for assembling a global network where the implicit links are dependent on global properties. Additionally, we explore alternative implicit link measures that remove global dependencies and thus allow for the global network to be assembled incrementally, as each participant makes independent contributions. Finally we evaluate three scalable similarity measures, two of which require a revision of the data model underlying our social annotations. ∗ Corresponding author.

We are experiencing a new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by systems like Wikipedia, twitter, and delicious.com, these environments are turning people into social information foragers and sharers. Groups interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton " to "Islam." PARC's Augmented Social Cognition researchers-- who come from cognitive psychology, computer science, HCI, CSCW, and other disciplines-- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Social Web systems like social bookmarking sites, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale. Here we summarize recent work and early findings such as: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize usergenerated content and metadata.

Web service, discovery, matchmaking, collaborative tagging, evaluation One of the key requirements for the success of Service Oriented Architecture is discoverability of Web services. Unfortunately, application of authoritatively defined taxonomies cannot cope with the volume of services published on the Web. Collaborative tagging claims to address this problem, but is impeded by the lack of structure to describe Web service functions and interfaces. In this paper we introduce structured collaborative tagging to improve Web service descriptions. Performance of the proposed technique obtained during the Cross-Evaluation track of the Semantic Service Selection 2009 contest is reported. Obtained results show that the proposed approach can be successfully used in both Web service tagging and querying. 1

The following paper examines ongoing work on Wigglestick, a mobile way-finding service for the urban pedestrian. It enables users to tag media at specific spots, make their location visible to approved friends, and find their way to desired places. Its usage of the divining rod as a metaphor for the development of the mobile application has encouraged a minimal abstracted visualization approach and a social navigation system based on user-generated location tags.

Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason; to augment their speed and capacity to acquire, produce, communicate, and use knowledge; and to advance collective and individual intelligence in socially mediated information environments. In this paper, we describe the emergence of this research endeavor, and summarize some results from the research. In particular, we have found that (1) analyses of conflicts and coordination in Wikipedia have shown us the scientific need to understand social sensemaking environments; and (2) information theoretic analyses of social tagging behavior in del.icio.us shows the need to understand human vocabulary systems.

Abstract—Unlimited vocabulary annotation of multimedia documents remains elusive despite progress solving the problem in the case of a small, fixed lexicon. Taking advantage of the repetitive nature of modern information and online media databases with independent annotation instances, we present an approach to automatically annotate multimedia documents that uses mining techniques to discover new annotations from similar documents and to filter existing incorrect annotations. The annotation set is not limited to words that have training data or for which models have been created. It is limited only by the words in the collective annotation vocabulary of all the database documents. A graph reinforcement method driven by a particular modality (e.g., visual) is used to determine the contribution of a similar document to the annotation target. The graph supplies possible annotations of a different modality (e.g., text) that can be mined for annotations of the target. Experiments are performed using videos crawled from YouTube. A customized precision-recall metric shows that the annotations obtained using the proposed method are superior to those originally existing for the document. These extended, filtered tags are also superior to a state-of-the-art semi-supervised technique for graph reinforcement learning on the initial user-supplied annotations. Index Terms—Data mining, graph theory, video annotation, video content analysis. I.