Warning: This paper may contain content that could be disturbing or offensive.Content moderation in online platforms faces persistent challenges due to the evolving complexity of user-generated content and the limitations of traditional rule-based and machine learning approaches.While recent advances in large language models (LLMs) have enabled more sophisticated moderation via direct prompting or fine-tuning, these approaches often exhibit limited generalization, interpretability, and adaptability to unseen or ambiguous cases.In this work, we propose a novel moderation framework that leverages analogical examples to enhance rule induction and decision reliability.Our approach integrates end-to-end optimization of analogical retrieval, rule generation, and moderation classification, enabling the dynamic adaptation of moderation rules to diverse content scenarios.Through comprehensive experiments, we demonstrate that our method significantly outperforms both rule-injected finetuning baselines and multi-stage static RAG pipelines in terms of moderation accuracy and rule quality.Further evaluations, including human assessments and external model generalization tests, confirm that our framework produces rules with better clarity, interpretability, and applicability.These findings show that analogical example-driven methods can advance robust, explainable, and generalizable content moderation in real-world applications.

Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Spatio-temporal time series forecasting has attracted great attention in various fields, including climate, power, and traffic forecasting. Recently, Spatio-temporal Graph Neural Networks (STGNNs) have shown promising performance in modeling spatial dependencies based on graph neural networks (GNNs) and temporal dependencies based on temporal learning modules. However, most STGNNs do not effectively integrate explicit and implicit relationships between nodes, nor do they adequately capture long and short-term time dependencies. To address these challenges, this paper presents a Quaternion Spatio-temporal Graph Neural Network(QSTGNN). Specifically, the quaternion spatio-temporal graph is constructed firstly, such that the information of both short and long-term time steps are preserved in quaternion feature tensor, and information of multiple explicit graphs and implicit graph are integrated in quaternion graph adjacency matrix. Then, two modules are designed: a 1D quaternion convolution module and a quaternion graph convolution module. In the 1D quaternion convolution module, complex temporal correlations among short and long-term time steps can be well exploited by 1D quaternion convolution operator based on the quaternion Hamilton product. In the quaternion graph convolution module, quaternion graph convolution is designed to characterize nonlinear dependencies among multiple spatial graphs, including explicit and implicit graphs. Extensive experiments are conducted on six datasets, and the results show that QSTGNN achieves state-of-the-art performances over the existing ten methods. Explainable analysis presents that multiple spatial correlations can accurately illustrate the traffic flow and road functional information in real traffic roads.