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2020 AAAI AAAI 2020

On Identifying Hashtags in Disaster Twitter Data

Abstract

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.1

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — hashtag extraction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Jishnu Ray Chowdhury , Cornelia Caragea , Doina Caragea

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Weakly Supervised Learning Machine Learning > Application Areas > Domain Adaptation Natural Language Processing > Applications > Text Classification Machine Learning > Learning Types > Multi-Task Learning Deep Learning > Learning Types > Multi-Task Learning

Keywords

multi-task learning text classification named entity recognition long short-term memory twitter datum disaster management hashtag extraction
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