CADNN: Class-Imbalanced Adversarial Neural Network for Unsupervised Domain Adaption in Emergency Events

Social networks are now the primary channels for sharing information about events, especially during emergencies. Identifying crucial information in these situations is intricate due to the absence of specific event labels and the prevalent label imbalance in social network data. Prior efforts in disaster event management have applied unsupervised domain adaptation (UDA) techniques. However, these efforts encounter difficulties when focusing on specific events and dealing with class-imbalance domain adaptation (CDA), often leading to modifications in data distribution. This article introduces a solution named class-imbalanced adversarial neural network (CADNN) to address UDA challenges in emergency events across diverse domains. First, an adversarial domain adaptation model is developed to achieve cross-domain feature representation. Second, unlike existing methods that rely on data augmentation, a cross-domain interpolation model is designed to generate aligned pairwise samples. This ensures that the generated samples preserve the original data distribution by preserving the sample centroid. Third, the sample interpolation is integrated with the adversarial domain adaptation model, optimizing the overall loss function to enhance generalization performance. Experimental results on four open-source datasets demonstrate that CADNN outperforms state-of-the-art models in recognizing emergency event information.