Abstract

Social web users are a very diverse group with varying interests, levels of expertise, enthusiasm, and expressiveness. As a result, the quality of content and annotations they create to organize content is highly variable. While several approaches have been proposed to mine social annotations, for example, to learn folksonomies that reflect how people relate narrower concepts to broader ones, these methods treat all users and the annotations they create uniformly. We propose a framework to automatically identify experts, i.e., knowledgeable users who create high quality annotations, and use their knowledge to guide folksonomy learning. We evaluate the approach on a large body of social annotations extracted from the photo sharing site Flickr. We show that using expert knowledge leads to more detailed and accurate folksonomies. Moreover, we show that including annotations from non-expert, or novice, users …

Date

October 9, 2011

Authors

Jeon-Hyung Kang, Kristina Lerman

Conference

2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing

Pages

215-222

Publisher

IEEE