On Identifying Hashtags in Disaster Twitter Data

• URL: http://arxiv.org/abs/2001.01323v1
• Date: Sun, 5 Jan 2020 22:37:17 GMT
• Title: On Identifying Hashtags in Disaster Twitter Data
• Authors: Jishnu Ray Chowdhury, Cornelia Caragea, Doina Caragea
• Abstract summary: We construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we 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%.
• Score: 55.17975121160699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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