The dynamics and semantics of collaborative tagging (original) (raw)
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The complex dynamics of collaborative tagging
Proceedings of the 16th …, 2007
The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.
The structure of collaborative tagging systems
Arxiv preprint cs/0508082, 2005
Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.
A Study on Community Formation in Collaborative Tagging Systems
2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008
Current collaborative tagging systems do not allow community members to easily view available data related to their communities. In this paper, we present our study on community formation centered around common interests, utilizing` data from del.icio.us. We propose an approach to clustering tags and users based on their similarity. We also report some practical results related to implicit grouping of tags and users.
Informatica Economica, 2013
Collaborative tagging has become a very popular way of annotation, thanks to the fact that any entity may be labeled by any individual based on his own reason. In this paper we present the results of the case study carried out on the basis of data gathered at different time intervals from the social tagging system developed and implemented on Întelepciune.ro. Analyzing collective data referring to the way in which community members associate different tags, we have observed that between tags, links are formed which become increasingly stable with the passing of time. Following the application of methodology specific to network analysis, we have managed to extract information referring to tag popularity, their influence within the network and the degree to which a tag depends upon another. As such, we have succeeded in determining different semantic structures within the collective tagging system and see their evolution at different stages in time. Furthermore, we have pictured the way in which tag recommendations can be executed and that they can be integrated within recommendation systems. Thus, we will be able to identify experts and trustworthy content based on different categories of interest.
Emergent Community Structure in Social Tagging Systems
Computing Research Repository, 2008
A distributed classification paradigm known as collaborative tagging has been widely adopted in new web applications designed to manage and share online resources. Users of these applications organize resources (web pages, digital photographs, academic papers) by associating with them freely chosen text labels, or tags. Here we leverage the social aspects of collaborative tagging and introduce a notion of resource distance based on the collective tagging activity of users. We collect data from a popular system and perform experiments showing that our definition of distance can be used to build a weighted network of resources with a detectable community structure. We show that this community structure clearly exposes the semantic relations among resources. The communities of resources that we observe are a genuinely emergent feature, resulting from the uncoordinated activity of a large number of users, and their detection paves the way to mapping emergent semantics in social tagging systems.
Patterns and Inconsistencies in Collaborative Tagging Systems: An Examination of Tagging Practices
Proceedings of The Asist Annual Meeting, 2006
This paper analyzes the tagging patterns exhibited by users of del.icio.us, to assess how collaborative tagging supports and enhances traditional ways of classifying and indexing documents. Using frequency data and co-word analysis matrices analyzed by multi-dimensional scaling, the authors discovered that tagging practices to some extent work in ways that are continuous with conventional indexing. Small numbers of tags tend to emerge by unspoken consensus, and inconsistencies follow several predictable patterns that can easily be anticipated. However, the tags also indicated intriguing practices relating to time and task which suggest the presence of an extra dimension in classification and organization, a dimension which conventional systems are unable to facilitate.
Journal of Information & Knowledge Management, 2010
This study investigates the contribution of collaborative tagging to the design of user-driven vocabularies in knowledge management systems (KMS). Three metrics, tag growth, tag reuse, and tag discrimination, were used to examine the evolution of the tagging vocabulary of the knowledge management community of interest in CiteULike over a three-year period. Results indicate a steady decrease in the number of unique tags over the four years, suggesting an increasing stability in the community vocabulary over time and the establishment of domain-specific vocabulary. Members reused each others' tags over time and exhibited increasingly collaborative tagging behaviour. Tag discrimination was high, with 4.11 distinct articles per tag. The stable and discriminatory nature of the community's tags suggests that collaborative tagging may serve as a useful resource for vocabulary choice or maintenance by KMS managers.
Usage patterns of collaborative tagging systems
Journal of Information Science, 2006
... 3. Delicious dynamics Del.icio.us, or Delicious, is a collaborative tagging system for web bookmarks that its creator, Joshua Schachter, calls a 'social bookmarks manager' [5]. We analyzed data from Delicious to uncover patterns among users, tags and URLs. ... T imes T ag Has B ...
Individual and social behavior in tagging systems
2009
In tagging systems users can annotate items of interest with freeform terms. A good understanding of the usage characteristics of such systems is necessary to improve the design of current and next generation tagging systems. To this end, this work explores three aspects of user behavior in CiteULike and Connotea, two systems that include tagging features to support online personalized management of scientific publications. First, this study characterizes the degree to which users re-tag previously published items and reuse tags: 10 to 20% of the daily activity can be characterized as re-tagging and about 75% of the activity as tag reuse. Second, we use the pairwise similarity between users' activity to characterize the interest sharing in these systems. We present the interest sharing distribution across the systems, show that this metric encodes information about existing usage patterns, and attempt to correlate interest sharing levels to indicators of collaboration such as co-membership in discussion groups and semantic similarity of tag vocabularies. Finally, we show that interest sharing leads to an implicit structure that exhibits a natural segmentation. Throughout the paper we discuss the potential impact of our findings on the design of mechanisms that support tagging systems.