TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Strohmaier, Markus AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity T2 - Proceedings of the 19th International World Wide Web Conference (WWW 2010) PB - ACM CY - Raleigh, NC, USA PY - 2010/04 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf M3 - KW - 2010 KW - collaborative_verbosity KW - emergentsemantics_factors KW - ibm-kde-tagging KW - itegpub KW - myown KW - ol_web2.0 KW - www KW - www2010 L1 - SN - N1 - N1 - AB - Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise??, and (iii) in learning ontologies. ER - TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Strohmaier, Markus AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity T2 - Proceedings of the 19th International World Wide Web Conference (WWW 2010) PB - ACM CY - Raleigh, NC, USA PY - 2010/04 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf M3 - KW - 2010 KW - collaborative_verbosity KW - emergentsemantics_factors KW - ibm-kde-tagging KW - itegpub KW - myown KW - ol_web2.0 KW - www KW - www2010 L1 - SN - N1 - N1 - AB - Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise�?, and (iii) in learning ontologies. ER - TY - CONF AU - Strohmaier, M. AU - Körner, C. AU - Kern, R. A2 - T1 - Why do users tag? Detecting users' motivation for tagging in social tagging systems T2 - International AAAI Conference on Weblogs and Social Media (ICWSM2010) PB - CY - Washington, DC, USA PY - 2010/05 M2 - VL - IS - SP - EP - UR - http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1497 M3 - KW - collaborative KW - folksonomy KW - intent KW - motivation KW - purpose KW - tagging KW - user KW - ol_web2.0 KW - emergentsemantics_factors L1 - SN - N1 - N1 - AB - While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question: 1.) What motivates users to tag resources, and in what ways is user motivation amenable to quantitative analysis? 2.) Does users' motivation for tagging vary within and across social tagging systems, and if so how? and 3.) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply the measures to datasets from 8 different tagging systems. Our results show that a) users' motivation for tagging varies not only across, but also within tagging systems, and that b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (i) the development of tag recommenders, (ii) the analysis of tag semantics and (iii) the design of search algorithms for social tagging systems. ER -