Effective tweets classification for disaster crisis based on ensemble of classifiers

Authors

  • Christopher Ifeanyi Eke
    Department of Computer Science, Faculty of Computing, Federal University of Lafia, P.M.B 146, Lafia, Nasarawa State, Nigeria
  • Kholoud Maswadi
    Department of Management Information Systems, Jazan University, Jazan 45142, Saudi Arabia
  • Musa Phiri
    School of Engineering and Technology, Mulungushi University, PO Box 80415, Kabwe, Zambia
  • Mulenga Mwege
    School of Engineering and Technology, Mulungushi University, PO Box 80415, Kabwe, Zambia
  • Mohammad Imran
    Department of Information Technology, Balochistan University of Information Technology, Engineering and Management Sciences, Airport Road, Baleli, Quetta, Pakistan
  • Dekera Kenneth Kwaghtyo
    Department of Computer Science, Faculty of Computing, Federal University of Lafia, P.M.B 146, Lafia, Nasarawa State, Nigeria
  • Akeremale Olusola Collins
    Department of Mathematics, Faculty of Science, Federal University of Lafia, P.M.B 146, Lafia, Nasarawa State, Nigeria

Keywords:

Disaster Crisis Management, Social Media Analytics, Twitter, Machine Learning Classifiers, Ensemble Methods, Feature Extraction

Abstract

In the field of disaster management, social media analytics has gained significant recognition. Social media platforms, particularly Twitter, have become an invaluable source for disseminating information during disasters, offering real-time updates on events, crisis reports, and casualty information. However, the deluge of information on social media can also be overwhelming, with a substantial amount of irrelevant content. To address this challenge, researchers leverage machine learning (ML) classifiers to automatically categorize disaster-related tweets. However, ML classifiers, while being effective, also face issues such as overfitting and class imbalance. This study proposes an ensemble-based approach that integrates a variety of linguistic and word embedding features, including Parts-Of-Speech (POS), hashtags, Term Frequency-Inverse Document Frequency (TF-IDF), GloVe, Word2Vec, and BERT. A range of supervised learning algorithms like Decision Trees, Logistic Regression, Support Vector Machines, and Random Forests, were evaluated individually and as part of ensemble methods like AdaBoost, Bagging, and Random Subspace. The results show that combining TF-IDF with word embeddings and using the AdaBoost ensemble model yields superior performance, achieving a classification accuracy of 98.92%. This represents a notable improvement over the conventional standalone classifiers and highlights the advantage of ensemble methods in enhancing model robustness and minimizing overfitting. The proposed approach demonstrates not only high predictive capacity but also scalability for real-time tweet filtering during emergencies. In addition to demonstrating the efficacy of ensemble methods in disaster tweet classification, this study also provides valuable insights for improving social media-based crisis response. It also establishes a foundation for future research, particularly in multi-lingual and multi-disaster scenarios.

Dimensions

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Published

2025-08-01

How to Cite

Effective tweets classification for disaster crisis based on ensemble of classifiers. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2675. https://doi.org/10.46481/jnsps.2025.2675

Issue

Volume 7, Issue 3, August 2025

Section

Computer Science
Copyright & Licensing

Copyright (c) 2025 Christopher Ifeanyi Eke, Kholoud Maswadi, Musa Phiri, Mulenga Mwege, Mohammad Imran, Dekera Kenneth Kwaghtyo, Akeremale Olusola Collins

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Effective tweets classification for disaster crisis based on ensemble of classifiers. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2675. https://doi.org/10.46481/jnsps.2025.2675

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