Enhancing Disaster Response and Public Safety with Advanced Social Media Analytics and Natural Language Processing
Received: 11 March 2024 | Revised: 26 March 2024 and 3 April 2024 | Accepted: 6 April 2024 | Online: 1 June 2024
Corresponding author: Mohd Anul Haq
Abstract
This study investigates the effectiveness of the DistilBERT model in classifying tweets related to disasters. This study achieved significant predictive accuracy through a comprehensive analysis of the dataset and iterative refinement of the model, including adjustments to hyperparameters. The benchmark model developed highlights the benefits of DistilBERT, with its reduced size and improved processing speed contributing to greater computational efficiency while maintaining over 95% of BERT's capabilities. The results indicate an impressive average training accuracy of 92.42% and a validation accuracy of 82.11%, demonstrating the practical advantages of DistilBERT in emergency management and disaster response. These findings underscore the potential of advanced transformer models to analyze social media data, contributing to better public safety and emergency preparedness.
Keywords:
NLP, ML, DL, big data analyticsDownloads
References
R. Prasad, A. U. Udeme, S. Misra, and H. Bisallah, "Identification and classification of transportation disaster tweets using improved bidirectional encoder representations from transformers," International Journal of Information Management Data Insights, vol. 3, no. 1, Apr. 2023, Art. no. 100154.
A. E. Yüksel, Y. A. Türkmen, A. Özgür, and B. Altınel, "Turkish Tweet Classification with Transformer Encoder," in Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), Varna, Bulgaria, Jun. 2019, pp. 1380–1387.
V. Porvatov and N. Semenova, "5q032e@SMM4H’22: Transformer-based classification of premise in tweets related to COVID-19." arXiv, Oct. 15, 2023.
V. Balakrishnan et al., "A Comprehensive Analysis of Transformer-Deep Neural Network Models in Twitter Disaster Detection," Mathematics, vol. 10, no. 24, Jan. 2022, Art. no. 4664.
C. Wang, P. Nulty, and D. Lillis, "Transformer-based Multi-task Learning for Disaster Tweet Categorisation." arXiv, Oct. 15, 2021.
G. S. Cheema, S. Hakimov, and R. Ewerth, "Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features." arXiv, Sep. 20, 2020.
G. A. Dima, D. C. Cercel, and M. Dascalu, "Transformer-based Multi-Task Learning for Adverse Effect Mention Analysis in Tweets," in Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, Mexico City, Mexico, Mar. 2021, pp. 44–51.
J. A. Benítez-Andrades, J.-M. Alija-Pérez, M. E. Vidal, R. Pastor-Vargas, and M. T. García-Ordás, "Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study," JMIR Medical Informatics, vol. 10, no. 2, Feb. 2022, Art. no. e34492.
S. K. Nigam and M. Shaheen, "Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation." arXiv, Mar. 08, 2022.
R. K. Das and D. T. Pedersen, "SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT." arXiv, Jan. 15, 2024.
W. Antoun, F. Baly, and H. Hajj, "AraBERT: Transformer-based Model for Arabic Language Understanding." arXiv, Mar. 07, 2021.
T. Zhao, J. Du, Y. Shao, and A. Li, "Aspect-Based Sentiment Analysis Using Local Context Focus Mechanism with DeBERTa," in 2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS), Tianjin, China, Sep. 2023.
Q. Deng, G. Cai, H. Zhang, Y. Liu, L. Huang, and F. Sun, "Enhancing situation awareness of public safety events by visualizing topic evolution using social media," in Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, Delft, The Netherlands, Feb. 2018, pp. 1–10.
P. Duraisamy, M. Duraisamy, M. Periyanayaki, and Y. Natarajan, "Predicting Disaster Tweets using Enhanced BERT Model," in 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Feb. 2023, pp. 1745–1749.
M. Aboualola, K. Abualsaud, T. Khattab, N. Zorba, and H. S. Hassanein, "Edge Technologies for Disaster Management: A Survey of Social Media and Artificial Intelligence Integration," IEEE Access, vol. 11, pp. 73782–73802, 2023.
N. Kankanamge, T. Yigitcanlar, A. Goonetilleke, and Md. Kamruzzaman, "Determining disaster severity through social media analysis: Testing the methodology with South East Queensland Flood tweets," International Journal of Disaster Risk Reduction, vol. 42, Jan. 2020, Art. no. 101360.
W. Sun, P. Bocchini, and B. D. Davison, "Applications of artificial intelligence for disaster management," Natural Hazards, vol. 103, no. 3, pp. 2631–2689, Sep. 2020.
F. Mlawa, E. Mkoba, and N. Mduma, "A Machine Learning Model for detecting Covid-19 Misinformation in Swahili Language," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10856–10860, Jun. 2023.
B. Ahmed, G. Ali, A. Hussain, A. Baseer, and J. Ahmed, "Analysis of Text Feature Extractors using Deep Learning on Fake News," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7001–7005, Apr. 2021.
M. A. Haq, "Smotednn: A novel model for air pollution forecasting and AQI classification," Computers, Materials and Continua, vol. 71, no. 1, pp. 1403–1425, 2022.
M. A. Haq, "CDLSTM: A novel model for climate change forecasting," Computers, Materials and Continua, vol. 71, no. 2, pp. 2363–2381, 2022.
A. Alabdulwahab, M. A. Haq, and M. Alshehri, "Cyberbullying Detection using Machine Learning and Deep Learning," International Journal of Advanced Computer Science and Applications, vol. 14, no. 10, pp. 424–432, 2023.
M. A. Haq, M. A. R. Khan, and M. Alshehri, "Insider Threat Detection Based on NLP Word Embedding and Machine Learning," Intelligent Automation and Soft Computing, vol. 33, no. 1, pp. 619–635, 2022.
Downloads
How to Cite
License
Copyright (c) 2024 Khalil Alharbi, Mohd Anul Haq
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
