# Sub-event detection from Twitter streams as a sequence labeling problem

arXiv: https://arxiv.org/abs/1903.05396 · HTML: https://mlsanity.com/paper/1903.05396/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation](https://mlsanity.com/paper/2505.03827/) (2025)
- [Traffic Event Detection as a Slot Filling Problem](https://mlsanity.com/paper/2109.06035/) (2021)
- [Creating Multimedia Summaries Using Tweets and Videos](https://mlsanity.com/paper/2203.08931/) (2022)
- [EnrichEvent: Enriching Social Data with Contextual Information for Emerging Event Extraction](https://mlsanity.com/paper/2307.16082/) (2023)

## Abstract (arXiv)

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).