While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. In this paper, we focus on problem (i) based on a distinction between two types of user motivations that we have identified in earlier work: Categorizers vs. Describers. To that end, we systematically define and evaluate a number of measures designed to discriminate between describers, i.e. users who use tags for describing resources as opposed to categorizers, i.e. users who use tags for categorizing resources. Subsequently, we present empirical findings from qualitative and quantitative evaluations of the measures on real world tagging behavior. In addition, we conducted a recommender evaluation in which we study the effectiveness of each of the presented measures and found the measure based on the tag content to be the most accurate in predicting the user behavior closely followed by a content independent measure. The overall contribution of this paper is the presentation of empirical evidence that tagging motivation can be approximated with simple statistical measures. Our research is relevant for (a) designers of tagging systems aiming to better understand the motivations of their users and (b) researchers interested in studying the effects of users' tagging motivation on the properties of resulting tags and emergent structures in social tagging systems

@inproceedings{koerner2010categorizers, author = {Körner, Christian and Kern, Roman and Grahsl, Hans-Peter and Strohmaier, Markus}, title = {Of categorizers and describers: an evaluation of quantitative measures for tagging motivation}, booktitle = {HT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {157--166}, url = {http://portal.acm.org/citation.cfm?id=1810617.1810645}, doi = {10.1145/1810617.1810645}, isbn = {978-1-4503-0041-4}, keywords = {categorizer, describer, folksonomy, motivation, tagging}, abstract = {While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. In this paper, we focus on problem (i) based on a distinction between two types of user motivations that we have identified in earlier work: Categorizers vs. Describers. To that end, we systematically define and evaluate a number of measures designed to discriminate between describers, i.e. users who use tags for describing resources as opposed to categorizers, i.e. users who use tags for categorizing resources. Subsequently, we present empirical findings from qualitative and quantitative evaluations of the measures on real world tagging behavior. In addition, we conducted a recommender evaluation in which we study the effectiveness of each of the presented measures and found the measure based on the tag content to be the most accurate in predicting the user behavior closely followed by a content independent measure. The overall contribution of this paper is the presentation of empirical evidence that tagging motivation can be approximated with simple statistical measures. Our research is relevant for (a) designers of tagging systems aiming to better understand the motivations of their users and (b) researchers interested in studying the effects of users' tagging motivation on the properties of resulting tags and emergent structures in social tagging systems} }

%0 = inproceedings %A = Körner, Christian and Kern, Roman and Grahsl, Hans-Peter and Strohmaier, Markus %B = HT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia %C = New York, NY, USA %D = 2010 %I = ACM %T = Of categorizers and describers: an evaluation of quantitative measures for tagging motivation %U = http://portal.acm.org/citation.cfm?id=1810617.1810645