With the recent rapid growth of social image hosting websites, such as Flickr, it is easier to construct a large database with tagged images. Social tags have been proven to be effective for providing keyword-based image retrieval and widely used on these websites, but whether they are beneficial for improving content-based image retrieval has not been well investigated in previous work. In this paper, we investigate whether and how social tags can be used for improving content-based image search results. We propose an unsupervised approach for automatic ranking without user interactions. It propagates visual and textual information on an image-tag relationship graph with a mutual reinforcement process. We conduct experiments showing that our approach can successfully use social tags for ranking and improving content-based social image search results, and performs better than other approaches.