Abstract

In recent years, social media applications like Twitter (X) have become important sources of real-time information in case of natural disasters. Nevertheless, the high velocity of unstructured and loud textual contents makes it difficult to precisely distinguish disaster-related information. The paper gives a comparative analysis of a baseline Bidirectional Encoder Representations from Transformers (BERT) model and a proposed hybrid model, HGTCA-BERT, on disaster tweet classification. The proposed HGTCA-BERT model integrates Hierarchical Graph (HG) structures to captures relationship between tweets, Temporal (T) features to model time-based information flow, and Cross-Attention (CA) mechanisms to enhance contextual understanding by combining textual, graph-based, and temporal representations. This multi-dimensional feature fusion enables the model to better understand the complex patterns present in disaster-related tweets. The experimental outcomes depict that HGTCA-BERT is better in performances, having an accuracy of (95%) much higher than BERT model (91%). This paper has demonstrated that HGTCA-BERT significantly outperforms the baseline BERT model by effectively leveraging structural and temporal dependencies alongside textual context.

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