Social media users and microbloggers post about a wide variety of (off-line) collective social activities as they participate in them, ranging from concerts and sporting events to political rallies and civil protests. In this context, people who take part in the same collective social activity often post closely related content from nearby locations at similar times, resulting in distinctive spatiotemporal patterns. Can we automatically detect these patterns and thus provide insights into the associated activities? In this paper, we propose a modeling framework for clustering streaming spatiotemporal data, the Spatial Dirichlet Hawkes Process (SDHP), which allows us to automatically uncover a wide variety of spatiotemporal patterns of collective social activity from geolocated online traces. Moreover, we develop an efficient, online inference algorithm based on Sequential Monte Carlo that scales to millions of geolocated posts. Experiments on synthetic data and real data gathered from Twitter show that our framework can recover a wide variety of meaningful social activity patterns in terms of both content and spatiotemporal dynamics, that it yields interesting insights about these patterns, and that it can be used to estimate the location from where a tweet was posted.