Multi-Task Disaster Tweet Classification Using Hybrid TF-IDF and Graph Convolutional Networks

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.