DPHFM: A Deep Parallel Hybrid Fusion Model for Disaster Tweet Classification on Twitter Data

Abstract

Abstract In recent years, disaster tweet classification has garnered significant attention in natural language processing (NLP) due to its potential to aid disaster response and emergency management. The goal of disaster tweet classification is to automate the identification of informative tweets containing information related to various types of disasters, such as floods, earthquakes, wildfires, and more. This classification task plays a crucial role in real-time monitoring, situational awareness, and timely response coordination during emergency situations. In this context, we propose a deep parallel hybrid fusion model (DPHFM) that combines features extracted from Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) as base learners. The extracted features from the base learners are combined using a fusion mechanism, and the resulting features are then reconstructed and supplied to a meta-learner as input for making predictions. The DPHFM is trained on disaster datasets, such as crisisMMD, which consists of seven natural disaster events. The model was thoroughly evaluated using various metrics, demonstrating an average performance improvement of 90–96%. Furthermore, the proposed model's performance surpassed that of other state-of-the-art models, showcasing its potential for disaster tweet classification using deep learning techniques.