On Identifying Hashtags in Disaster Twitter Data

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

Authors

• Jishnu Ray Chowdhury
• Cornelia Caragea Computer Science, University of Illinois Chicago
• Doina Caragea

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