Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data
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Abstract (arXiv)
This paper presents our research on leveraging social media Big Data and AI to support hurricane disaster emergency response. The current practice of hurricane emergency response for rescue highly relies on emergency call centres. The more recent Hurricane Harvey event reveals the limitations of the current systems. We use Hurricane Harvey and the associated Houston flooding as the motivating scenario to conduct research and develop a prototype as a proof-of-concept of using an intelligent agent as a complementary role to support emergency centres in hurricane emergency response. This intelligent agent is used to collect real-time streaming tweets during a natural disaster event, to identify tweets requesting rescue, to extract key information such as address and associated geocode, and to visualize the extracted information in an interactive map in decision supports. Our experiment shows promising outcomes and the potential application of the research in support of hurricane emergency response.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 14 Jun 2021.
Builds on
7 of 29 references.
References · 7
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2016Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messagescited by 43
- 2020CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embeddingcited by 25
- 2019Location reference identification from tweets during emergencies: A deep learning approachcited by 14
- 2019City-level Geolocation of Tweets for Real-time Visual Analyticscited by 4
- 2017End-to-end Network for Twitter Geolocation Prediction and Hashingcited by 3
- 2019A Deep Learning Approach for Tweet Classification and Rescue Scheduling for Effective Disaster Managementcited by 3
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Citing papers per year: 2024: 1.
1 paper.