A Comparative Study of Deep Learning Methods for the Detection and Classification of Natural Disasters from Social Media

Abstract

Disaster Management, defined as a coordinated social effort to successfully prepare for and respond to disasters, can benefit greatly as an industrial process from modern Deep Learning methods. Disaster prevention organizations can benefit greatly from the processing of disaster response data. In an attempt to detect and subsequently categorise disaster-related information from tweets via tweet text analysis, a Feedforward Neural Network (FNN), a Convolutional Neural Network, a Bi-directional Long Short-Term Memory (BLSTM), as well as several Transformer-based network architectures, namely BERT, DistilBERT, Albert, RoBERTa and DeBERTa, are employed. The two defined main tasks of the work presented in this paper are: (1) distinguishing tweets into disaster related and non relevant ones, and (2) categorising already labeled disaster tweets into eight predefined natural disaster categories. These supported types of natural disasters are earthquakes, floods, hurricanes, wildfires, tornadoes, explosions, volcano eruptions and general disasters. To achieve this goal, several accessible related datasets are collected and combined to suit the two tasks. In addition, the combination of preprocessing tasks that is most beneficial for inference is investigated. Finally, experiments have been conducted using bias mitigation techniques.