  News events and social media are composed of evolving storylines, which
capture public attention for a limited period of time. Identifying storylines
requires integrating temporal and linguistic information, and prior work takes
a largely heuristic approach. We present a novel online non-parametric Bayesian
framework for storyline detection, using the distance-dependent Chinese
Restaurant Process (dd-CRP). To ensure efficient linear-time inference, we
employ a fixed-lag Gibbs sampling procedure, which is novel for the dd-CRP. We
evaluate on the TREC Twitter Timeline Generation (TTG), obtaining encouraging
results: despite using a weak baseline retrieval model, the dd-CRP story
clustering method is competitive with the best entries in the 2014 TTG task.
