Comparative Analysis of Machine Learning Models for Real-Time Disaster Tweet Classification: Enhancing Emergency Response with Social Media Analytics
DOI:
10.47709/brilliance.v4i1.3669Keywords:
Disaster Response, Social Media Analytics, Natural Language Processing, Machine Learning Classification, Emergency Management SystemsDimension Badge Record
Abstract
In the realm of disaster management, the real-time analysis of social media data, particularly from Twitter, has become indispensable. This study investigates the efficacy of various machine learning models in classifying tweets pertaining to disaster scenarios, with the goal of bolstering emergency response systems. A dataset of tweets, categorized as related or unrelated to disasters, underwent a rigorous preprocessing regimen to facilitate the evaluation of five distinct machine learning models: Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks. The performance of these models was assessed based on accuracy, precision, recall, and F1 score. The results indicated that the SVM model excelled, achieving an accuracy of 89%, precision of 88%, recall of 89%, and an F1 score of 88%, making it the most robust for text classification tasks within the context of disaster-related data. The LSTM model also performed notably well, with an accuracy of 87%, precision of 86%, recall of 87%, and F1 score of 86%, underscoring the potential of deep learning models in processing sequential data. In comparison, Naïve Bayes, Random Forest, and Logistic Regression models demonstrated moderate performance, with accuracy and F1 scores in the range of 76-77% and 72-73%, respectively. These insights are crucial for the development of advanced social media monitoring tools that can significantly enhance the timeliness and precision of crisis response. The research not only highlights the necessity of selecting appropriate machine learning models for specific NLP tasks but also sets the stage for future investigations into the integration of hybrid analytical frameworks. This study establishes a foundation for leveraging machine learning to transform social media data into actionable intelligence, thereby contributing to more effective disaster management and community safety strategies.
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References
Alshayeb, M. J., Hang, H. T., Shohan, A. A. A., & Bindajam, A. A. (2024). Novel optimized deep learning algorithms and explainable artificial intelligence for storm surge susceptibility modeling and management in a flood-prone island. Natural Hazards, 1–30.
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2021). A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks. Expert Systems with Applications, 164, 114006.
AO, S. H., & Mak, A. K. Y. (2021). Regenerative crisis, social media publics and Internet trolling: A cultural discourse approach. Public Relations Review, 47(4), 102072.
Beedasy, J., Zuniga, A. F. S., Chandler, T., & Slack, T. (2020). Online community discourse during the Deepwater Horizon oil spill: an analysis of Twitter interactions. International Journal of Disaster Risk Reduction, 51, 101870.
Bhoi, A., Pujari, S. P., & Balabantaray, R. C. (2020). A deep learning-based social media text analysis framework for disaster resource management. Social Network Analysis and Mining, 10, 1–14.
Carrigan, M., & Fatsis, L. (2021). The public and their platforms: Public sociology in an era of social media. Policy Press.
Chai, C. P. (2023). Comparison of text preprocessing methods. Natural Language Engineering, 29(3), 509–553.
Chaudhary, L., Girdhar, N., Sharma, D., Andreu-Perez, J., Doucet, A., & Renz, M. (2023). A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities. IEEE Transactions on Computational Social Systems.
Devaraj, A., Murthy, D., & Dontula, A. (2020). Machine-learning methods for identifying social media-based requests for urgent help during hurricanes. International Journal of Disaster Risk Reduction, 51, 101757.
Drouhot, L. G., Deutschmann, E., Zuccotti, C. V, & Zagheni, E. (2023). Computational approaches to migration and integration research: promises and challenges. Journal of Ethnic and Migration Studies, Vol. 49, pp. 389–407. Taylor & Francis.
Durham, J., Chowdhury, S., & Alzarrad, A. (2023). Unveiling Key Themes and Establishing a Hierarchical Taxonomy of Disaster-Related Tweets: A Text Mining Approach for Enhanced Emergency Management Planning. Information, 14(7), 385.
Egger, R., & Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in Sociology, 7, 886498.
Fadhel, M. A., Duhaim, A. M., Saihood, A., Sewify, A., Al-Hamadani, M. N. A., Albahri, A. S., … Gu, Y. (2024). Comprehensive Systematic Review of Information Fusion Methods in Smart Cities and Urban Environments. Information Fusion, 102317.
Ghanem, F. A., Padma, M. C., & Alkhatib, R. (2023). Automatic Short Text Summarization Techniques in Social Media Platforms. Future Internet, 15(9), 311.
Grace, R. (2021). Overcoming barriers to social media use through multisensor integration in emergency management systems. International Journal of Disaster Risk Reduction, 66, 102636.
Hamood Alsamhi, S., Hawbani, A., Shvetsov, A. V, Kumar, S., & others. (2023). Advancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications. Advances in Human-Computer Interaction, 2023.
Hu, Y., Mai, G., Cundy, C., Choi, K., Lao, N., Liu, W., … Joseph, K. (2023). Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages. International Journal of Geographical Information Science, 37(11), 2289–2318.
Huang, D., Wang, S., & Liu, Z. (2021). A systematic review of prediction methods for emergency management. International Journal of Disaster Risk Reduction, 62, 102412.
Iglesias, C. A., Favenza, A., & Carrera, Á. (2020). A big data reference architecture for emergency management. Information, 11(12), 569.
Jaramillo, M., Pavón, W., & Jaramillo, L. (2024). Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review. Data, 9(1), 13.
Kanellopoulos, V., Trianatafyllou, V., Koutsojannis, C., & Lekkas, E. (2023). The Role of Social Media to the Natural Disaster or Crisis Management.
Karimiziarani, M., Jafarzadegan, K., Abbaszadeh, P., Shao, W., & Moradkhani, H. (2022). Hazard risk awareness and disaster management: Extracting the information content of twitter data. Sustainable Cities and Society, 77, 103577.
Khan, W., Daud, A., Khan, K., Muhammad, S., & Haq, R. (2023). Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends. Natural Language Processing Journal, 100026.
Knox Clarke, P., & Campbell, L. (2020). Decision-making at the sharp end: a survey of literature related to decision-making in humanitarian contexts. Journal of International Humanitarian Action, 5, 1–14.
Koranga, T., Hazari, R., & Das, P. (2023). Disaster Tweets Classification for Multilingual Tweets Using Machine Learning Techniques. International Conference on Computation Intelligence and Network Systems, 117–129.
Kour, H., & Gupta, M. K. (2022). An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM. Multimedia Tools and Applications, 81(17), 23649–23685.
Krishnamurthi, R., Kumar, A., Gopinathan, D., Nayyar, A., & Qureshi, B. (2020). An overview of IoT sensor data processing, fusion, and analysis techniques. Sensors, 20(21), 6076.
Maladry, A., Lefever, E., Van Hee, C., & Hoste, V. (2023). The limitations of irony detection in dutch social media. Language Resources and Evaluation, 1–32.
Mani, Z. A., & Goniewicz, K. (2023). Adapting disaster preparedness strategies to changing climate patterns in Saudi Arabia: A rapid review. Sustainability, 15(19), 14279.
Mardjo, A., & Choksuchat, C. (2022). HyVADRF: Hybrid VADER--Random Forest and GWO for Bitcoin Tweet Sentiment Analysis. IEEE Access, 10, 101889–101897.
Mirbabaie, M., Bunker, D., Stieglitz, S., Marx, J., & Ehnis, C. (2020). Social media in times of crisis: Learning from Hurricane Harvey for the coronavirus disease 2019 pandemic response. Journal of Information Technology, 35(3), 195–213.
Mirza, F. K., Gürsoy, A. F., Baykacs, T., Hekimouglu, M., & Pekcan, Ö. (2023). Residual LSTM neural network for time dependent consecutive pitch string recognition from spectrograms: a study on Turkish classical music makams. Multimedia Tools and Applications, 1–29.
Oralbekova, D., Mamyrbayev, O., Othman, M., Kassymova, D., & Mukhsina, K. (2023). Contemporary approaches in evolving language models. Applied Sciences, 13(23), 12901.
Peek, L., Tobin, J., Adams, R. M., Wu, H., & Mathews, M. C. (2020). A framework for convergence research in the hazards and disaster field: The natural hazards engineering research infrastructure CONVERGE facility. Frontiers in Built Environment, 6, 110.
Roztocki, N., Strzelczyk, W., & Weistroffer, H. R. (2023). The role of e-government in disaster management: A review of the literature. Journal of Economics and Management, 45(1), 1–25.
Son, J. (2023). Quick-and-wide propagation of disaster tweets: Why it matters and how to measure it. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 54(4), 11–30.
Stepanenko, V. (2023). Disaster Tweets.
Zheng, T., Wu, F., Law, R., Qiu, Q., & Wu, R. (2021). Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach. International Journal of Hospitality Management, 92, 102658.
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Accepted Date: 2024-03-01
Published Date: 2024-03-08
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