@article{Nath2026, 
author = {Basudev Nath and Deepak Sahoo and Sudhansu Shekhar Patra and Hassan Alkhiri and Subrata Chowdhury and Sheraz Aslam and Kainat Mustafa},
title = {Multi-Task Disaster Tweet Classification Using Hybrid TF-IDF and Graph Convolutional Networks},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {2},
pages = {91},
keywords = {Natural language processing, tweet classification, graph neural networks, deep learning},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.073486},
doi = {10.32604/cmc.2026.073486},
abstract = {Accurate, up to date, and quick information related to any disaster supports disaster management team/authorities to perform quick, easy, and cost-effective response to enhance rescue operations to alleviate the possible loss of lives, financial risks, and properties. Due to damaged infrastructure in disaster-affected areas, social media is the only way to share/ exchange real time information. Therefore, ‘X’ (formerly Twitter) has become a major platform for disseminating real-time information during disaster events or emergencies, i.e., floods and earthquake. Rapid identification of actionable content is critical for effective humanitarian response; however, the brief and noisy nature of tweets makes automated classification challenging. To tackle this problem, this study proposes a hybrid classification framework that integrates term frequency–inverse document frequency (TF-IDF) features with graph convolutional networks (GCNs) to enhance disaster-related tweet analysis. The proposed model performs three classification tasks: identifying disaster-related tweets (achieving 94.47% accuracy), categorizing disaster types (earthquake, flood, and non-disaster) with 91.78% accuracy, and detecting aid requests such as food, donations, and medical assistance (94.64% accuracy). By combining the statistical strengths of TF-IDF with the relational learning capabilities of GCNs, the model attains high accuracy while maintaining computational efficiency and interpretability. The results demonstrate the framework’s strong potential for real-time disaster response, offering valuable insights to support emergency management systems and humanitarian decision-making.}
}