Towards Rare Social Event Prediction via Mediator Learning

Rare social events are infrequent yet influential incidents. Predicting such events is practically significant yet inherently challenging due to their extreme scarcity in web-based event stream. Existing studies view this task as an imbalanced classification problem and adopt static rebalancing methods to mitigate scarcity. However, they (1) ignore inter-event dependency that represents the interactions between different event streams, failing to capture precursors that lead to rare events and fundamentally limits performance. They (2) overlook intra-event dependency between different time points within single event stream, which prevents the model from adapting to shifting event patterns and degrades its generalization ability. To this end, we propose a novel Mediator Learning (ML) framework, which introduces mediators to explicitly model complex dependencies within web-based event streams. Specifically, we propose (1) Precursor Event Router (PER) that utilizes an information-theoretic routing approach to extract precursor events as mediators from massive event streams. Based on extracted mediators, (2) Conditional Hierarchical Graph Network (CHG) is introduced to model observed events, mediators and rare events into bottom-up graph levels, where its upper-level propagation is conditioned on bottom-level probability distribution. Jointly, these two modules decompose the imbalanced task into two more balanced stages, which not only mitigates the scarcity of rare events but also explicitly model inter-event dependencies, so as to capture precursor events leading to target rare events. Finally, we design (3) Adaptive Information Regularizer (AIR) to optimize the two stages. It dynamically adjusts the information flow between two stages, which models intra-event dependencies and facilitate adaption to drifting event patterns. We theoretically reveal the effectiveness of ML by framing it within Information Bottleneck (IB) principle. Extensive experiments show our superior accuracy and interpretability compared with SOTA.