Intelligent Emergency Notification Mobile Service via Multi-Task BERT Models

Publication Date

1-1-2023

Document Type

Conference Proceeding

Publication Title

Proceedings - 2023 11th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2023

DOI

10.1109/MobileCloud58788.2023.00014

First Page

51

Last Page

58

Abstract

Mobile social networks have the potential to become the most effective platform for delivering urgent information during natural disaster events. Social media applications like Twitter are ideal for real-Time news delivery but lack many important features to become an effective emergency notification system. The most significant challenge is determining which news is an emergency and whether it is relevant to the user. In this project, we propose an intelligent emergency notification social network application that can automatically perform data mining from online social media platforms, filter out non-emergency and unrelated news, and deliver only classified emergency notifications to users. Rather than using multiple separate systems or machine learning models to achieve our goals, we propose a joint Multi-class Text Classification model based on BERT to filter out non-emergency tweets and classify the type of emergencies in one end-To-end neural network model. Additionally, we train a Named Entity Recognition (NER) model to extract locations from the classified emergency news. We evaluated our system in multiple historical emergency events and demonstrated the effectiveness of emergency news delivery using various performance metrics.

Funding Number

2148353

Funding Sponsor

National Science Foundation

Keywords

BERT, emergency notification, Natural language processing, Social network, text classification

Department

Computer Engineering

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