sosnotes11-10

A Case Study on Emergent Semantics in Communities.

Elke Michlmayr

In this paper, a method for selecting subsets of the meta-data provided by a folksonomy that adhere to the principle of interest-based locality was developed. The resulting data can be applied for simulating peers and their contents in a peer-to-peer environment. The properties of the test sets that were retrieved by using this method were analysed and discussed in order to prove that the proposed method selects subsets that have similar properties. Comparing the meta-data produced by the folksonomy to meta-data created by the DMOZ open directory project at the data level revealed that there are major differences between them. Finally, we showed that centrally provided lists of popular items have only small influences on the properties of these items.

Semiotic dynamics in online social communities.

Ciro Cattuto

A distributed classification paradigm known as collaborative tagging has been successfully deployed in large-scale web applications designed to manage and share diverse online resources. Users of these applications organize resources by associating with them freely chosen text labels, or tags. Here we regard tags as basic dynamical entities and study the semiotic dynamics underlying collaborative tagging. We collect data from a popular system and focus on tags associated with a given resource. We find that the frequencies of tags obey to a generalized Zipf’s law and show that a Yule–Simon process with memory can be used to explain the observed frequency distributions in terms of a simple model of user behavior.

Usage patterns of collaborative tagging systems.

Golder, Huberman

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 dynamic 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 dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.

Collaborative thesaurus tagging the Wikipedia way

This paper explores the system of categories that is used to classify articles in Wikipedia. It is compared to collaborative tagging systems like del.icio.us and to hierarchical classification like the Dewey Decimal Classification (DDC). Specifics and commonalities of these systems of subject indexing are exposed. Analysis of structural and statistical properties (descriptors per record, records per descriptor, descriptor levels) shows that the category system of Wikimedia is a thesaurus that combines collaborative tagging and hierarchical subject indexing in a special way.