Machine Learning Based a Comparative Analysis for Detecting Tweets of Earthquake Victims Asking for Help in The 2023 Turkey-Syria Earthquake

Abstract

Two major earthquakes in Kahramanmaraş on February 6, 2023, 9 hours apart, affected many countries, especially Turkey and Syria. It caused the death and injury of thousands of people. Earthquake survivors shared their help on social media after the earthquake. While people under the rubble shared some posts, some were for living materials. There were also posts unrelated to the earthquake. It is essential to analyze social media shares to plan the process management effectively, save time, and reach the victims as soon as possible. For this reason, about 500 tweets about the 2023 Turkey-Syria earthquake were analyzed in this study. The tweets were classified according to their content as user tweets under debris and user tweets requesting life material. Popular machine learning methods such as DT, kNN, LR, MNB, RF, SVM, and XGBoost were compared in detail. Experimental results showed that RF has over 99% classification accuracy.

Keywords

• Earthquake
• Machine learning
• Random forest
• Sentiment analysis

References

1. S. Kwayu, M. Abubakre, and B. Lal, “The influence of informal social media practices on knowledge sharing and work processes within organizations,” International Journal of Information Management, vol. 58, 102280, 2021.
2. H. Shirado, G. Iosifidis, L. Tassiulas, and N. A. “Christakis, Resource sharing in technologically defined social networks,” Nature Communications, vol. 10, no. 1, pp. 1079. 2019.
3. K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: tasks, approaches and applications,” Knowledge-based systems, vol. 89, pp. 14-46, 2015.
4. J. Y. Jung and M. Moro, “Multi‐level functionality of social media in the aftermath of the Great East Japan Earthquake,” Disasters, vol. 38, pp. 123-s143, 2014.
5. J. B. Houston, J. Hawthorne, M. F. Perreault, E. H. Park, M. Goldstein Hode, M. R. Halliwell, and S. A. “Griffith, Social media and disasters: a functional framework for social media use in disaster planning, response, and research,” Disasters, vol. 39, no. 1, pp. 1-22, 2015.
6. D. Yates, and S. Paquette, “Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake,” International Journal of Information Management, vol. 31, no. 1, pp. 6-13, 2011.
7. S. Ni, H. Sun, P. Somerville, D. A. Yuen, C. Milliner, H. Wang, and Y. Cui, “Complexities of the Turkey-Syria doublet earthquake sequence,” The Innovation, vol. 4, no. 3, 100431, 2023.
8. S. Mendon, P. Dutta, A. Behl, and S. Lessmann, “A hybrid approach of machine learning and lexicons to sentiment analysis: Enhanced insights from twitter data of natural disasters,” Information Systems Frontiers, vol. 23, pp. 1145-1168, 2021.

9. S. Behl, A. Rao, S. Aggarwal, S. Chadha, and H. S. Pannu, “Twitter for disaster relief through sentiment analysis for COVID-19 and natural hazard crises,” International Journal of Disaster Risk Reduction, vol. 55, 102101, 2021.
10. A. Bhoi, S. P. Pujari and R. C. Balabantaray, “A deep learning-based social media text analysis framework for disaster resource management,” Social Network Analysis and Mining, 10, 1-14. (2020).
11. N. Chaudhuri, and I. Bose, “Exploring the role of deep neural networks for post-disaster decision support,” Decision Support Systems, vol. 130, 113234, 2020.
12. S. Wang, Z. Li, Y. Wang, and Q. Zhang, “Machine learning methods to predict socia media disaster rumor refuters,” International Journal of Environmental Research and Public Health, vol. 16, no. 8, pp. 1452, 2019.
13. G. A. Ruz, P. A. Henríquez, and A. Mascareño, “Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers,” Future Generation Computer Systems, vol. 106, pp. 92-104, 2020.
14. Y. Kryvasheyeu, H. Chen, E. Moro, P. Van Hentenryck, and M. Cebrian, “Performance of social network sensors during Hurricane Sandy.” PLoS one, 10(2), e0117288, 2015.
15. L. Huang, P. Shi, H. Zhu, and T. Chen, “Early detection of emergency events from social media: A new text clustering approach.” Natural Hazards, 111(1), 851-875, 2022.
16. A. Kumar, J. P. Singh, N. P. Rana, and Y. K. Dwivedi, “Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster.” Information Systems Frontiers, 25(4), 1589-1604, 2023.
17. M. Kaya, and B. Hasan Şakir, "Classification of Parkinson speech data by metric learning." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-5. IEEE, 2017.
18. Ž. Vujović, (2021). Classification model evaluation metrics. International Journal of Advanced Computer Science and Applications, 12(6), 599-606.
19. Turkey Earthquake Relief Tweets Dataset, Available:https://www.kaggle.com/datasets/ulkutuncerkucuktas/turkey-earthquake-relief-tweets-dataset. [Accessed: April. 20, 2023].
20. A. Şenol, M. Kaya, and Y. Canbay, “A comparison of tree data structures in the streaming data clustering issue.” Journal of the Faculty of Engineering and Architecture of Gazi University, 39(1), 217-231, 2024.
21. S. B. Kotsiantis, “Decision trees: a recent overview,” Artificial Intelligence Review, vol. 39, pp. 261-283, 2013.
22. H. Bhavsar and A. Ganatra, “A comparative study of training algorithms for supervised machine learning,” International Journal of Soft Computing and Engineering (IJSCE), vol. 2, no. 4, pp. 2231-2307, 2012.
23. S. Zhang, X. Li, M. Zong, X. Zhu, and D. Cheng, “Learning k for knn classification,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 8, no. 3, pp. 1-19, 2017.
24. X. Su, X. Yan, and C. L. Tsai, “Linear regression,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 4, no. 3, pp. 275-294, 2012.
25. D. Maulud, and A. M. Abdulazeez, “A review on linear regression comprehensive in machine learning,” Journal of Applied Science and Technology Trends, vol. 1, no. 4, pp. 140-147, 2020.
26. L. Jiang, S. Wang, C. Li, and L. Zhang, “Structure extended multinomial naive Bayes. Information Sciences,” vol. 329, pp. 346-356, 2016.
27. S. Wang, L. Jiang and C. Li, “Adapting naive Bayes tree for text classification,” Knowledge and Information Systems, vol. 44, pp. 77-89, 2015.
28. M. M. Ghiasi and S. Zendehboudi, “Application of decision tree-based ensemble learning in the classification of breast cancer,” Computers in biology and medicine, vol. 128, 104089, 2021.
29. T. Amraee, and S. Ranjbar, “Transient instability prediction using decision tree technique,” IEEE Transactions on power systems, vol. 28, no. 3, pp. 3028-3037, 2013.
30. J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189-215, 2020.
31. T. Chen, and C. Guestrin, Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. San Francisco, California, USA, 785-794. 26, 2016.
32. I. H. Sarker, Machine Learning: Algorithms, RealWorld Applications and Research Directions. SN Computer Science, vol. 2, no. 160, pp. 1-21, 2021.