On Identifying Hashtags in Disaster Twitter Data

  • Jishnu Ray Chowdhury University of Illinois at Chicago
  • Cornelia Caragea University of Illinois at Chicago
  • Doina Caragea Kansas State University


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.1

AAAI Special Technical Track: AI for Social Impact