@Article{cmc.2026.084547,
AUTHOR = {Sheraz Ali Hassan, Hamid Masood Khan, Muhammad Javed, Mohd Faizal Bin Yusof, Jamil Abedalrahim Jamil Alsayaydeh, Fida Muhammad Khan, Inam Ullah},
TITLE = {Attention-Enhanced Hybrid Deep Learning for Disaster-Related Tweet Classification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/28037},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2026.084547}
}