On Identifying Hashtags in Disaster Twitter Data
This work addresses the challenge of filtering actionable information from disaster tweets for responders and researchers, but it is incremental as it builds on existing methods with a new dataset.
The paper tackled the problem of automatically identifying useful hashtags in disaster-related tweets to improve information search during disasters, achieving an F1-score of 92.22% with a Long Short Term Memory-based model in a Multi-Task Learning framework.
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.