BibSonomy publications for /user/mstrohm/socialSat Nov 13 16:46:11 CET 2010Engineering societies in the agents world1--18{Engineering social order}2000MUSTREAD agents networks social Wed Nov 10 10:20:34 CET 2010science59961194{The Spread of Behavior in an Online Social Network Experiment}3292010MUSTREAD behavior networks social Mon Apr 19 19:11:25 CEST 2010Proc. of the Semantic Search 2010 Workshop (SemSearch2010)aprilThe Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams2010folksonomy myown networks social tagging twitter taggingsurvey Sun Mar 28 12:48:20 CEST 2010Toronto, Canada21st ACM SIGWEB Conference on Hypertext and Hypermedia (HT 2010)JuneOf Categorizers and Describers: An Evaluation of Quantitative Measures for Tagging Motivation2010agents folksonomy goals mining motivation social tagging myown Fri Feb 26 17:28:25 CET 2010New York, NY, USASIGWEB Newsl.Winter1--6A call for social tagging datasets2010datasets social tagging myown Tagging represents a new, user-driven form of indexing and labeling resources on the web. The notion of "social tagging" usually refers to web-based systems that are supporting users in collaboratively tagging and sharing resources, such as Delicious, Flickr and others. In recent years, social tagging systems have emerged as an interesting alternative to labeling and linking resources on the web. This development has created an interesting opportunity for the Hypertext research community to gain new perspectives and understanding about the dynamics and nature of tagging and linking in large scale, participative Hypertext systems.Fri Feb 26 17:24:25 CET 2010WWW2010: Proceedings of the 19th International World Wide Web Conference, Raleigh, NC, USA, April 26-30Stop Thinking, Start Tagging: Tag Semantics Emerge From Collaborative Verbosity2010motivation myown semantic social tagging taggingsurvey Fri Nov 16 11:00:10 CET 2007New York, NY, USACSCW '04: Proceedings of the 2004 ACM conference on Computer supported cooperative work212--221Using social psychology to motivate contributions to online communities2004MUSTREAD goals social social-factors web-science web2.0 Mon Sep 24 11:24:59 CEST 2007New York, NY, USAIUI '07: Proceedings of the 12th international conference on Intelligent user interfaces52--61Collecting community wisdom: integrating social search & social navigation2007search social web-science web2.0 The goal of this paper is to detail the integration of two "social Web" technologies - social search and social navigation - and to highlight the benefits of such integration on two levels. Firstly, both technologies harvest and harness "community wisdom" and in an integrated system each of the search and navigation components can benefit from the additional community wisdom gathered by the other when assisting users to locate relevant information. Secondly, by integrating search and browsing we facilitate the development of a unique interface that effectively blends search and browsing functionality as part of a seamless social information access service. This service allows users to effectively combine their search and browsing behaviors. In this paper we will argue that this integration provides significantly more than the simple sum of the parts.Wed Aug 08 15:38:42 CEST 2007Berlin / HeidelbergThe Semantic Web: Research and Applications, 3rd European Semantic Web Conference, ESWC 2006, Budva, MontenegroJune411-426Lecture Notes in Computer ScienceInformation Retrieval in Folksonomies: Search and Ranking40112006folksonomy search social web-science web2.0 11-14Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.