# On Identifying Hashtags in Disaster Twitter Data

arXiv: https://arxiv.org/abs/2001.01323 · HTML: https://mlsanity.com/paper/2001.01323/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Transformer-based Multi-task Learning for Disaster Tweet Categorisation](https://mlsanity.com/paper/2110.08010/) (2021)
- [Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response](https://mlsanity.com/paper/2402.13528/) (2024)
- [Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning](https://mlsanity.com/paper/2408.09914/) (2024)
- [Attend and select: A segment selective transformer for microblog hashtag generation](https://mlsanity.com/paper/2106.03151/) (2021)
- [#REVAL: a semantic evaluation framework for hashtag recommendation](https://mlsanity.com/paper/2305.18330/) (2023)
- [Information Cascade Prediction under Public Emergencies: A Survey](https://mlsanity.com/paper/2404.01319/) (2024)
- [Social Media Data Mining of Human Behaviour during Bushfire Evacuation](https://mlsanity.com/paper/2512.01262/) (2025)

## Abstract (arXiv)

Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as 92.22%. The dataset, code, and other resources are available on Github.