Year: 2025
Type: article
Abstract: In the field of disaster crisis management, the utilization of social media platforms has gained significant recognition. It helps in disseminating and gathering information during disasters, offering real-time updates on events, infrastructure damage reports, and casualty information. However, the information comes with a substantial amount of irrelevant content. Some researchers have utilized machine learning classifiers for classification, which has become ineffective. Thus, this study proposes an ensemble-based approach to disaster tweet classification, using a wide array of linguistic and word embedding features, Additionally, we investigate various supervised learning algorithms and ensemble classifiers for resolving this issue. Our findings reveal that the ensemble feature sets, specifically, the fusion of TF-IDF and word embeddings, when coupled with Bagging, achieved a classification accuracy of 98.92%. This research highlights the potential of leveraging machine learning and ensemble techniques on disaster tweet classification, providing insights for improving real-time disaster response efforts.
Authors: Christopher Ifeanyi Eke, Kholoud Maswadi, Musa Phiri, Mulenga, Mohammad Imran, Dekera Kwaghtyo, Akeremale Olusola Collins
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $140
OpenAlex ID: W4410739358