Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCL$_{SED}$, and its variant - UCL-EC$_{SED}$, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012\_100, Events2018\_100, and CrisisLexT\_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.

提出了一种新颖的基于不确定性引导的类别不平衡学习框架（UCL_SED）及其变体（UCL-EC_SED），用于不平衡社交事件检测任务，通过增强模型对不确定类别的泛化能力来改进整体模型性能。在实验中，模型在几乎所有类别中，特别是那些不确定的类别中，显著改善了社交事件表示和分类任务。

基于不确定性引导的不均衡社会事件检测的边界学习