Beyond Deep Learning: A Two-Stage Approach to Classifying Disaster Events and Needs

Social media’s real-time nature has transformed it into a critical tool for disaster response, and for that this study explores the use of tweets for classifying disaster types and identifying humanitarian needs in the aftermath of various disaster events.We compare traditional machine learning models like Random Forest and Support Vector Machines with the deep learning technique, BERT. While BERT demonstrates promising results, a key finding lies in the performance of the voting classifier ensemble, a combination of traditional models. This ensemble achieves accuracy comparable to BERT and even surpasses it. Furthermore, the ensemble boasts exceptional training and inference speeds, making it ideal for real-time applications in disaster response scenarios.Our work investigates the continued value of traditional machine learning methods. By "dusting off" these models we can achieve competitive performance while maintaining computational efficiency. Ultimately, this study empowers humanitarian organizations to leverage the power of text classification for extracting crucial insights from social media data, leading to more effective and targeted responses in times of crisis.