# Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data

arXiv: https://arxiv.org/abs/2106.07114 · HTML: https://mlsanity.com/paper/2106.07114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages](https://mlsanity.com/paper/1605.05894/) (2016)
- [CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding](https://mlsanity.com/paper/2005.06627/) (2020)
- [Location reference identification from tweets during emergencies: A deep learning approach](https://mlsanity.com/paper/1901.08241/) (2019)
- [City-level Geolocation of Tweets for Real-time Visual Analytics](https://mlsanity.com/paper/1910.02213/) (2019)
- [End-to-end Network for Twitter Geolocation Prediction and Hashing](https://mlsanity.com/paper/1710.04802/) (2017)
- [A Deep Learning Approach for Tweet Classification and Rescue Scheduling for Effective Disaster Management](https://mlsanity.com/paper/1908.01456/) (2019)

## Built on by (most cited first, 1 of 1)

- [Large Language Models Powered Multiagent Ensemble for Mitigating Hallucination and Efficient Atrial Fibrillation Annotation of ECG Reports](https://mlsanity.com/paper/2410.16543/) (2024)

## 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.