  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.
