Social Propagation: Boosting Social Annotations for Web Mining

This paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. In particular, we focus on propagating social annotations of web pages (e.g., annotations in Del.icio.us). Social annotations are novel resources and valuable in many web applications, including web search and browsing. Although they are developing fast, social annotations of web pages cover only a small proportion (< 0.1%) of the World Wide Web. To alleviate the low coverage of annotations, a general propagation model based on Random Surfer is proposed. Specifically, four steps are included, namely basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. The model is evaluated on a dataset of 40,422 web pages randomly sampled from 100 most popular English sites and ten famous academic sites. Each page's annotations are obtained by querying the history interface of Del.icio.us. Experimental results show that the proposed model is very effective in increasing the coverage of annotations while still preserving novel properties of social annotations. Applications of propagated annotations on web search and classification further verify the effectiveness of the model.