Research Paper: Analyzing Tag Distributions in Folksonomies for Resource Classification
Arkaitz Zubiaga, Raquel Martınez, and Victor Fresno
NLP & IR Group @ UNED (National Distance Education University)
Paper was Presented at KSEM 2011, 5th International Conference on Knowledge Science, Engineering and Management
Recent research has shown the usefulness of social tags as a data source to feed resource classification. Little is known about the effect of settings on folksonomies created on social tagging systems. In this work, we consider the settings of social tagging systems to further understand tag distributions in folksonomies. We analyze in depth the tag distributions on three large-scale social tagging datasets, and ana- lyze the effect on a resource classification task. To this end, we study the appropriateness of applying weighting schemes based on the well-known TF-IDF for resource classification. We show the great importance of set- tings as to altering tag distributions. Among those settings, tag sugges- tions produce very different folksonomies, which condition the success of the employed weighting schemes. Our findings and analyses are relevant for researchers studying tag-based resource classification, user behavior in social networks, the structure of folksonomies and tag distributions, as well as for developers of social tagging systems in search of an appropriate setting.
Direct to Full Text Paper (12 pages; PDF)
About Gary Price
Gary Price (firstname.lastname@example.org) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.