Fast Disaster Event Detection from Social Media: An Active Learning Method

Abstract

This study introduces a novel framework for fast disaster event detection from social media,hich incorporates active learning into Bi-directional Long Short-Term Memory (Bi-LSTM). In theace of increasing disaster-related information on social media platforms, our method addresses thehallenge of efficiently processing vast, unstructured datasets to accurately extract crucial disasterrelatednsights. Leveraging the contextual processing strengths of Bi-LSTM and the data efficiencyf active learning, our approach significantly outperforms several baseline models in accuracy,recision, recall, and F1-score. We demonstrate that our method effectively balances these metrics,nsuring reliable disaster detection while minimizing false positives and negatives. The studylso explores the impact of various model parameters on performance, offering insights for futureptimizations. Despite its promising results, the study acknowledges limitations such as datauality, representativeness, and computational resource requirements. Future work will focus onnhancing data preprocessing, expanding language and scenario applicability, and integrating theodel with additional technologies for broader disaster management applications.