In this work, we frame the problem of sub-event detection in Twitter streams as a sequence labeling task.
we (i) propose a binary classification baseline model that outperforms state-of-the-art approaches for sub-event detection (presence/absence), (ii) establish a strong baseline that additionally predicts sub-event types, and then (iii) extend this baseline model with the idea of exchanging chronological information between sequential posts, and (iv) prove it to be beneficial in almost all examined architectures.