2011/10/21 by Jeon-Hyung Kang, Kristina Lerman, Kang, Jeon-Hyung +1
Computer Science · Physics and Astronomy · #Expert finding and Q&A systems #FOS: Computer and information sciences #FOS: Physical sciences #Information Retrieval (cs.IR) #Multimodal Machine Learning Applications #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Topic Modeling #cs.IR #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1110.4851
The Third IEEE International Conference on Social Computing (SocialCom2011)
arxiv created 2011/10/21 · openalex publication_date 2011/10/21 · arxiv updated 2011/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 also 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 photosharing 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 leads to more comprehensive folksonomies than experts' knowledge alone.