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On Identifying Hashtags in Disaster Twitter Data

2020-01-05 · Jishnu Ray Chowdhury, Cornelia Caragea, Doina Caragea

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.

📄 PDF Abstract BibTeX arXiv:2001.01323

Code (1)

JRC1995/Tweet-Disaster-Keyphrase 공식 구현 tf

Tasks

Disaster ResponseMulti-Task Learning

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