Properties of corpora, such as the diversity of vocabulary and how tightly related texts cluster together, impact the best way to cluster short texts. We examine several such properties in a variety of corpora and track their effects on various combinations of similarity metrics and clustering algorithms. We show that semantic similarity metrics outperform traditional n-gram and dependency similarity metrics for kmeans clustering of a linguistically creative dataset, but do not help with less creative texts. Yet the choice of similarity metric interacts with the choice of clustering method. We find that graphbased clustering methods perform well on tightly clustered data but poorly on loosely clustered data. Semantic similarity metrics generate loosely clustered output even when applied to a tightly clustered dataset. Thus, the best performing clustering systems could not use semantic metrics.