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• Proposed a deep hybrid CNN model for informative tweet classification. • Combined the character and word embedding methods to capture message semantics. • Validation done with cross-domain datasets. The current study offers a hybrid convolutional neural networks (CNN) model that filters relevant posts and categorises them into several humanitarian classifications using both character and word embedding of textual content. The distinct embeddings for words and characters are used as input to the CNN model's various channels. A hurricane, flood, and wildfire dataset are used to validate the proposed model. The model performed similarly across all datasets, with the F1-score ranging from 0.66 to 0.71. Because it uses existing social media posts and may be used as a layer with any social media, the model provides a sustainable solution for disaster analysis. With domain-specific training, the suggested approach can be used to locate useful information in other domains such as traffic accidents and civil unrest also. [ABSTRACT FROM AUTHOR]