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.
@article{arxiv.2001.01323,
title = {On Identifying Hashtags in Disaster Twitter Data},
author = {Jishnu Ray Chowdhury and Cornelia Caragea and Doina Caragea},
journal= {arXiv preprint arXiv:2001.01323},
year = {2020}
}