HTCNN-Attn: a fine-grained hierarchical multi-label deep learning model for disaster emergency information intelligent extraction from social media.

ABSTRACT: To address the challenge of extracting fine-grained emergency information from noisy social media during disasters, we propose HTCNN-Attn, a hierarchical multi-label deep learning model. It integrates a three-level tree-structured labeling architecture, Transformer-based global feature extraction, convolutional neural network (CNN) layers for local pattern capture, and a hierarchical attention mechanism. The model employs a hierarchical loss function to enforce label consistency across three levels: binary disaster filtering (Level 1), mid-level category classification (Level 2), and fine-grained subcategory extraction (Level 3). Experiments on the Appen and HumAID datasets demonstrate superior performance, achieving an accuracy of 0.9725, a Micro-F1 of 0.7402, and a hierarchical consisten

SUBMITTER: Li S 

PROVIDER: S-EPMC12453697 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature