Detection of Urgent Messages Shared on Twitter during an Earthquake using the Deep Learning Method

Year 2024, Issue: 3, 63 - 84, 22.01.2025

Mücahit Söylemez , Ali Öztürk

https://doi.org/10.26650/JODA.1606403

Abstract

The ability of Twitter to provide real-time information during disasters is becoming more widely acknowledged, making it an essential forum for people to voice their concerns and ask for help during emergencies. These platforms can speed up the distribution of help, but they are also prone to false information, which might make disaster response more difficult. Using a carefully selected dataset of 10,200 tweets that have been extensively preprocessed and tokenized for reliable training and validation, this study uses deep learning models, such as LSTM, BLSTM, and BLSTMA, to classify tweets during earthquake events into two categories: “under the debris” and “not under the debris.” The model performance was further improved via hyperparameter adjustment, which included neuron counts, dropout rates, dimensions, and embedding types. The results of this study showed that while the BLSTMA model had the best accuracy (96.64%) and F1 score (0.9116), conventional machine learning techniques like XGBoost and SVM. However, in other measurements, it was shown that standard machine learning techniques like SVM and XGBoost performed better. Using Bag of Words vectorisation, SVM obtained 95.81% accuracy and an F1 score of 0.9579, whereas XGBoost earned 95.84% accuracy and an F1 score of 0.9584. By demonstrating the usefulness of the BLSTMA model in real-time disaster response and the complementary advantages of conventional approaches in the analysis of complex disaster data, these findings highlight the significance of customising machine learning and deep learning approaches to particular tasks.

Keywords

Deep Learning, Natural Language Processing, Disaster Management, Twitter Analysis, Emergency Message Detection

References

• Al Jazeera (2023). “Quake victims stuck under rubble take to social media for help.” Available: https://www.aljazeera. com/news/2023/2/7/quake-victims-stuck-under-rubble-take-to-social-media-for-help google scholar
• Euronews (2023). “How Twitter helped find survivors trapped beneath rubble after Turkey’s earthquakes.” Available: https://www.euronews.com/next/2023/02/10/how-twitter-helped-find-survivors-trapped-beneath-rubble-after-turkeys-earthquakes google scholar
• Powers, C. J., Devaraj, A., Ashqeen, K., Dontula, A., Joshi, A., Shenoy, J., & Murthy, D. (2023). Using artificial intelligence to identify emergency messages on social media during a natural disaster: A deep learning approach. International Journal of Information Management Data Insights, 3(1), 100164. https://doi.org/10.1016/j. jjimei.2023.100164 google scholar
• Bhere, P., Upadhyay, A., Chaudhari, K., & Ghorpade, T. (2020). Classifying Informatory Tweets during Disaster Using Deep Learning. ITM Web of Conferences, 32, 03025. https://doi.org/10.1051/itmconf/20203203025 google scholar
• Kumar, A., Singh, J. P., & Saumya, S. (2019). A Comparative Analysis of Machine Learning Techniques for Disaster-Related Tweet Classification. In 2019 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1-6). IEEE. https://doi.org/10.1109/R10-HTC47129.2019.9042443 google scholar
• Behl, S., Rao, A., Aggarwal, S., Chadha, S., & Pannu, H. S. (2021). Twitter for disaster relief through sentiment analysis for COVID-19 and natural hazard crises. International Journal of Disaster Risk Reduction, 55, 102101. https://doi.org/10.1016/j.ijdrr.2021.102101 google scholar
• Madichetty, S., & Sridevi, M. (2020). A stacked convolutional neural network for detecting the resource tweets during a disaster. Multimedia Tools and Applications, 80, 3927-3949. https://doi.org/10.1007/s11042-020-09873-8 google scholar
• Sit, M.A., Koylu, C., & Demir, I. (2019). Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma. International Journal of Digital Earth, 12, 1205-1229. https://doi.org/10.1080/17538947.2018.1563219 google scholar
• Mijwel, M. M. (2015, April). History of Artificial Intelligence. Computer Science, 3-4 google scholar
• Kang, Y., Cai, Z., Tan, C., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172. https://doi.org/10.1080/23270012.2020 .1756939 google scholar
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press. (pp. 1-20) google scholar
• Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. https://doi.org/10.1037/h0042519 google scholar
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press. (pp. 168-171). google scholar
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press. (pp. 373-383). google scholar
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https:// doi.org/10.1162/neco.1997.9.8.1735 google scholar
• Graves, A., Mohamed, A.-R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 6645-6649). IEEE. https://doi. org/10.1109/ICASSP.2013.6638947 google scholar
• Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. (2016). Attention-based bidirectional long short-term memory networks for relation classification. In K. Erk & N. A. Smith (Eds.), Proceedings of the 54th Annual Meeting ofthe Association for Computational Linguistics (Volume 2: Short Papers) (pp. 207-212). Association for Computational Linguistics. https://doi.org/10.18653/v1/P16-2034 google scholar
• Zhong, G., Lin, X., Chen, K., Li, Q., & Huang, K. (2020). Long short-term attention. In Lecture Notes in Computer Science (pp. 45-54). Springer. https://doi.org/10.1007/978-3-030-39431-8_5 google scholar
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https:// doi.org/10.1162/neco.1997.9.8.1735 google scholar
• M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” in IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2673-2681, Nov. 1997, doi: 10.1109/78.650093 google scholar
• Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention is All you Need. Neural Information Processing Systems. google scholar