Disaster Tweets Classification using BERT-Based Language Model

TL;DR

This paper develops a BERT-based language model to classify disaster-related tweets, aiding real-time emergency detection and response by analyzing social media posts during crises.

Contribution

It introduces a novel BERT-based model specifically tailored for disaster tweet classification, enhancing emergency detection capabilities.

Findings

Achieved high accuracy in disaster tweet classification

Demonstrated effectiveness in real-time emergency detection

Improved monitoring of social media for disaster response

Abstract

Social networking services have became an important communication channel in time of emergency. The aim of this study is to create a machine learning language model that is able to investigate if a person or area was in danger or not. The ubiquitousness of smartphones enables people to announce an emergency they are observing in real-time. Because of this, more agencies are interested in programmatically monitoring Twitter (i.e. disaster relief organizations and news agencies). Design a language model that is able to understand and acknowledge when a disaster is happening based on the social network posts will become more and more necessary over time.