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In recent years, social media—especially Twitter—has become a key platform for disaster response, enabling the rapid spread of critical information. The substantial amount of tweets during crises necessitates the creation of an effective classification system to distinguish pertinent disaster-related content from irrelevant Tweets. This study examines various machine learning and deep learning techniques for classifying disaster-related tweets, ultimately introducing a hybrid RoBERTa-BiLSTM model that outperforms conventional methods. Our research assesses multiple models, including Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM). We also fine-tuned transformer-based models like DistilBERT to determine their effectiveness in capturing contextual nuances. The dataset, obtained from Kaggle, contains labeled disaster and non-disaster tweets, with rigorous preprocessing applied to enhance text representation. Results of experiments demonstrate that the proposed RoBERTa-BiLSTM model achieves the highest classification accuracy of 84%, surpassing other methods in performance. This outcome highlights the advantage of combining transformer-based contextual understanding with sequential learning capabilities. Our findings highlight the potential of hybrid deep learning architectures in disaster management, offering a reliable solution for real-time crisis monitoring and information extraction from social media. Future research may explore real-time deployment, multilingual support, and multi-modal integration to further enhance practical applicability.

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Natural Language Processing with Disaster Tweets: Predicting Crisis Events with Social Media Using Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Saddam Hossain,
  • Doina Logofătu

摘要

In recent years, social media—especially Twitter—has become a key platform for disaster response, enabling the rapid spread of critical information. The substantial amount of tweets during crises necessitates the creation of an effective classification system to distinguish pertinent disaster-related content from irrelevant Tweets. This study examines various machine learning and deep learning techniques for classifying disaster-related tweets, ultimately introducing a hybrid RoBERTa-BiLSTM model that outperforms conventional methods. Our research assesses multiple models, including Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM). We also fine-tuned transformer-based models like DistilBERT to determine their effectiveness in capturing contextual nuances. The dataset, obtained from Kaggle, contains labeled disaster and non-disaster tweets, with rigorous preprocessing applied to enhance text representation. Results of experiments demonstrate that the proposed RoBERTa-BiLSTM model achieves the highest classification accuracy of 84%, surpassing other methods in performance. This outcome highlights the advantage of combining transformer-based contextual understanding with sequential learning capabilities. Our findings highlight the potential of hybrid deep learning architectures in disaster management, offering a reliable solution for real-time crisis monitoring and information extraction from social media. Future research may explore real-time deployment, multilingual support, and multi-modal integration to further enhance practical applicability.