In this work we experiment with well-known algorithms in this field and propose two new methods based on adaptive island cuts. Using the Social20 dataset (a collection gathered from Flickr) we evaluate several community detection methods, with quantitative analysis of each algorithm in terms of the relative number of communities (which we interpret as topics) that they produce and their sizes, as well as qualitative analysis of topics per human judgement. Our evaluation shows that it is possible to extract ad-hoc topics for search results using community detection, but that different community detection methods produce very different results. In particular, our proposed methods produce more compact and less noisy clusters as well as less relative recall when compared to methods that produce much larger clusters.