A contrastive topic-aware attentive framework with label encodings for post-disaster resource classification

Abstract

Social media has emerged as a critical platform for disseminating real-time information during disasters. However, extracting actionable resource data, such as needs and availability, from this vast and unstructured content remains a significant challenge, leading to delays in identifying and allocating resources, with severe consequences for affected populations. This study addresses this challenge by investigating the potential of label and topic features, combined with text embeddings, to enhance the performance and efficiency of resource identification from social media data. We propose Crisis Resource Finder (CRFinder), a novel framework that leverages label encoding and topic features to extract richer contextual information, uncover hidden patterns, and reveal the true context of disaster resources. CRFinder incorporates novel techniques such as multi-level text-label attention and contrastive text-topic attention to capture semantic and thematic nuances within the textual data. Additionally, our approach employs topic injection and selective contextualization techniques to enhance thematic relevance and focus on critical information, which is pivotal for targeted relief efforts. Extensive experiments demonstrate the significant improvements achieved by CRFinder over existing state-of-the-art methods, with average weighted F1-score gains of 7.12%, 6.44%, and 7.89% on datasets from the Nepal earthquake, Italy earthquake, and Chennai floods, respectively. By providing timely and accurate insights into resource needs and availabilities, CRFinder has the potential to revolutionize disaster response efforts.