TY  - EJOU
AU  - Hassan, Sheraz Ali 
AU  - Khan, Hamid Masood 
AU  - Javed, Muhammad 
AU  - Yusof, Mohd Faizal Bin 
AU  - Alsayaydeh, Jamil Abedalrahim Jamil 
AU  - Khan, Fida Muhammad 
AU  - Ullah, Inam 

TI  - Attention-Enhanced Hybrid Deep Learning for Disaster-Related Tweet Classification
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - The increasing frequency of natural and human-induced disasters has intensified the need for reliable methods to identify crisis-relevant information from Twitter/X streams. However, tweets are often short, noisy, informal, ambiguous, and context-dependent, making disaster-related tweet classification challenging. This study proposes a lightweight attention-enhanced hybrid deep learning framework for binary disaster-related tweet classification. The framework integrates CNN-based local feature extraction, recurrent contextual modeling, static pre-trained word embeddings, class weighting, training-only data augmentation, and learned neural attention. Two architectures, CNN–LSTM–Attention and CNN–BiGRU–Attention, are evaluated on the labeled Kaggle Disaster Tweets dataset using a leakage-aware protocol in which augmentation is applied only after dataset partitioning. Conventional machine-learning, standalone deep-learning, and Transformer-based baselines, including BERT, RoBERTa, DistilBERT, and CrisisBERT, are evaluated under the same experimental setting. Experimental results show that CNN–BiGRU–Attention achieves the highest accuracy of 94.81%, while CNN–LSTM–Attention provides slightly higher ROC-AUC and disaster-class recall. Compared with Transformer-based baselines, the proposed models achieve competitive performance with substantially fewer trainable parameters. These findings indicate that lightweight attention-enhanced hybrid models can provide an effective accuracy–efficiency trade-off for disaster-related Twitter/X monitoring, although broader validation across unseen crisis events, platforms, languages, and real-time streams remains necessary.
KW  - Disaster tweet classification; Twitter/X monitoring; crisis communication; hybrid deep learning; CNN–LSTM; CNN–BiGRU; learned attention; transformer baselines; data augmentation

DO  - 10.32604/cmc.2026.084547