SatCoBiLSTM: Self-attention based hybrid deep learning framework for crisis event detection in social media

Abstract

Social media platforms have emerged as vital sources of sharing real-time information during crises, enabling users to share critical details about disasters and ensuring timely awareness. This study addresses the daunting challenge of effectively detecting crisis events from a vast, noisy stream of short text data. To address the above-mentioned issue, we present a self-attention-based hybrid deep learning framework, SatCoBiLSTM, designed to extract hierarchical textual features and crucial information from textual data. The proposed model integrates a multi-scale convolution and BiLSTM layer to extract local and contextual relations, alongside a self-attention layer to select and focus on the most relevant crisis features. Various experiments have been conducted on three benchmark real-world crisis datasets to examine the proposed model’s performance. SatCoBiLSTM achieved an impressive F1-score of 96%, 94%, and 95% on the three public datasets. It also shows a promising improvement in the F1-score, compared to the state-of-the-art (SOTA) and baseline methods, by 1%, 1%, and 6%, respectively, showing its effectiveness in crisis event detection. An ablation analysis has been carried out to investigate the validity of each integrated layer in our model. The SatCoBiLSTM model’s capability to identify crisis-related information highlights its potential to enhance real-time awareness during disasters. Eventually, the study advances crisis event detection and lays the foundation for future research to process and handle short-textual data in noisy environments.