Joint Neighbor Semantic Modeling for Mitigating Over-squashing in Heterogeneous Social Event Detection

Social Event Detection (SED) aims to identify real-world events from social media streams, where event semantics are distributed across heterogeneous interactions among messages, users, entities, and temporal relations. Graph Neural Networks (GNNs) have become a dominant paradigm for SED by aggregating multi-hop neighborhood information. However, as the receptive field expands, exponentially growing neighborhood evidence must be compressed into fixed-dimensional node representations, leading to over-squashing and the loss of critical event semantics. Existing approaches primarily mitigate over-squashing through structural rewiring or topology augmentation, while paying limited attention to how complementary semantic evidence should be organized before message propagation. In this work, we attribute over-squashing to a semantic bottleneck induced by independent neighbor aggregation, in which complementary event cues are fragmented, diluted, and ultimately lost during message passing. To address this problem, we propose **J**oint **N**eighbor **S**emantic **M**odeling (**JNSM**), a semantic bottleneck-aware framework that explicitly organizes complementary neighbors into target-specific semantic units before propagation. JNSM first performs dual neighbor selection to construct candidate pairs from semantically close and distant neighbors. It then evaluates each candidate using a target-centered semantic gain criterion that balances semantic contribution, redundancy suppression, and cancellation correction. High-gain neighbor pairs are selected as joint semantic units and encoded into learnable semantic unit representations that capture complementary interactions and target-aware context. Finally, JNSM performs semantic-driven local rewiring and semantic unit propagation, allowing complementary event evidence to be better preserved and integrated into the target node. Extensive experiments on three heterogeneous SED benchmarks demonstrate that JNSM achieves strong and robust performance across diverse social-media graphs. Further analyses show that JNSM better preserves event evidence in semantically diverse neighborhoods, providing empirical support for semantic organization before propagation as an effective approach to mitigating over-squashing in heterogeneous SED.