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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 1, 2024.
Abstract: In the wake of disasters, timely access to accurate information about on-the-ground situation is crucial for effective disaster response. In this regard, social media (SM) like Twitter have emerged as an invaluable source of real-time user-generated data during such events. However, accurately detecting informative content from large amounts of unstructured user-generated data under such time-sensitive circumstances remains a challenging task. Existing methods predominantly rely on non-contextual language models, which fail to accurately capture the intricate context and linguistic nuances within the disaster-related tweets. While some recent studies have explored context-aware methods, they are based on computationally demanding transformer architectures. To strike a balance between effectiveness and computational efficiency, this study introduces a new context-aware transfer learning approach based on DistilBERT for the accurate detection of disaster related informative content on SM. Our novel approach integrates DistilBERT with a Feed Forward Neural Network (FFNN) and involves multistage finetuning of the model on balanced benchmark real-world disaster datasets. The integration of DistilBERT with an FFNN provides a simple and computationally efficient architecture, while the multistage finetuning facilitates a deeper adaptation of the model to the disaster domain, resulting in improved performance. Our proposed model delivers significant improvements compared to the state-of-the-art (SOTA) methods. This suggests that our model not only addresses the computational challenges but also enhances the contextual understanding, making it a promising advancement for accurate and efficient disaster-related informative content detection on SM platforms.
Saima Saleem and Monica Mehrotra, “Context-Aware Transfer Learning Approach to Detect Informative Social Media Content for Disaster Management” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150167
@article{Saleem2024,
title = {Context-Aware Transfer Learning Approach to Detect Informative Social Media Content for Disaster Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150167},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150167},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {1},
author = {Saima Saleem and Monica Mehrotra}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.