  This paper introduces improved methods for sub-event detection in social
media streams, by applying neural sequence models not only on the level of
individual posts, but also directly on the stream level. Current approaches to
identify sub-events within a given event, such as a goal during a soccer match,
essentially do not exploit the sequential nature of social media streams. We
address this shortcoming by framing the sub-event detection problem in social
media streams as a sequence labeling task and adopt a neural sequence
architecture that explicitly accounts for the chronological order of posts.
Specifically, we (i) establish a neural baseline that outperforms a graph-based
state-of-the-art method for binary sub-event detection (2.7% micro-F1
improvement), as well as (ii) demonstrate superiority of a recurrent neural
network model on the posts sequence level for labeled sub-events (2.4%
bin-level F1 improvement over non-sequential models).
