Social media are becoming the preferred channel to report and discuss events happening around the world. The data from these channels can be used to detect ongoing events in real-time. A typical approach is to use event detection methods, usually consisting of a clustering phase, in which similar documents are grouped together, and then an analysis of the clusters to decide whether they deal with real-world events. To cluster together similar documents, content representation models are critical. In this paper, we individually compare the performances of different social media documents content representation models used during the clustering phase, exploiting lexical, semantic and social media specific features, like tags and URLs. To the best of our knowledge, these models are usually individually exploited in this context. We investigate their complementarity and propose to combine them.