摘要： 社交媒体中虚假信息的传播对公共安全构成严峻威胁，现有基于外部知识增强的检测方法常因知识冗余与噪声干扰导致性能受限。本文引入基于条件最优传输（Conditional Optimal Transport，COT）的关键特征提取方法，将原始文本的全局语义作为先验条件，最小化与大语言模型（Large Language Modal，LLM）生成的外部知识间的条件KR（Kantorovich-Rubinstein）距离，提取外部知识的关键特征。进一步设计空间序列映射模块，显式建模文本位置信息以保留结构特征，并结合交叉注意力机制和余弦相似度动态加权外部知识，实现外部知识的自适应融合。在公共数据集Weibo与GossipCop上的实验表明，提取外部知识后的检测方法的F1分数分别超越最优基线模型3.1%与1.3%，消融实验验证了COT模块与空间序列映射模块的有效性。此外，参数敏感性分析显示模型在超参数波动下保持稳定（F1波动<±0.015），证明其强鲁棒性。本研究为知识增强的虚假信息检测提供了新的理论范式与技术路径。

Abstract: The dissemination of misinformation on social media poses severe threats to public safety. Existing external knowledge-enhanced detection methods often suffer from performance limitations due to knowledge redundancy and noise interference. This paper introduces a key feature extraction method based on Conditional Optimal Transport (COT), which leverages the global semantics of raw text as prior conditions to minimize the conditional Kantorovich-Rubinstein (KR) distance between external knowledge generated by Large Language Models (LLMs) and original texts, thereby extracting critical features from external knowledge. Furthermore, we design a spatial-sequential mapping module to explicitly model textual positional information for preserving structural features, while integrating cross-attention mechanisms and cosine similarity to dynamically weight external knowledge for adaptive fusion. Experiments on public datasets Weibo and GossipCop demonstrate that the proposed knowledge-enhanced detection method outperforms the best baseline models by 3.1% and 1.3% in F1-score, respectively. Ablation studies confirm the effectiveness of both the COT module and spatial-sequential mapping module. Additionally, parameter sensitivity analysis reveals the model's stability under hyperparameter fluctuations (F1-score fluctuations remained below ±0.015), proving its strong robustness. This study provides novel theoretical paradigms and technical pathways for knowledge-enhanced misinformation detection.