In recent years, multimodal multidomain fake news detection has garnered increasing attention. Nevertheless, this direction presents two significant challenges: (1) Failure to Capture Cross-Instance Narrative Consistency: existing models usually evaluate each news in isolation, fail to capture cross-instance narrative consistency, and thus struggle to address the spread of cluster based fake news driven by social media; (2) Lack of Domain Specific Knowledge for Reasoning: conventional models, which rely solely on knowledge encoded in their parameters during training, struggle to generalize to new or data-scarce domains (e.g., emerging events or niche topics). To tackle these challenges, we introduce Retrieval-Augmented Multimodal Model for Fake News Detection (RAMM). First, RAMM employs a Multimodal Large Language Model (MLLM) as its backbone to capture cross-modal semantic information from news samples. Second, RAMM incorporates an Abstract Narrative Alignment Module. This component adaptively extracts abstract narrative consistency from diverse instances across distinct domains, aggregates relevant knowledge, and thereby enables the modeling of high-level narrative information. Finally, RAMM introduces a Semantic Representation Alignment Module, which aligns the model's decision-making paradigm with that of humans - specifically, it shifts the model's reasoning process from direct inference on multimodal features to an instance-based analogical reasoning process. Extensive experimental results on three public datasets validate the efficacy of our proposed approach. Our code is available at the following link: https://github.com/li-yiheng/RAMM

翻译：近年来，多模态多领域假新闻检测日益受到关注。然而，该方向面临两大挑战：（1）无法捕捉跨实例叙事一致性：现有模型通常孤立评估每条新闻，未能捕捉跨实例的叙事一致性，难以应对社交媒体驱动的集群式假新闻传播；（2）缺乏领域特定推理知识：传统模型仅依赖训练时编码在参数中的知识，难以泛化至新领域或数据稀缺领域（如新兴事件或小众话题）。为应对这些挑战，我们提出了检索增强多模态假新闻检测模型（RAMM）。首先，RAMM采用多模态大语言模型（MLLM）作为主干网络，以捕捉新闻样本中的跨模态语义信息。其次，RAMM引入抽象叙事对齐模块，该模块自适应地从不同领域的多样实例中提取抽象叙事一致性，聚合相关知识，从而实现对高层次叙事信息的建模。最后，RAMM提出语义表征对齐模块，将模型的决策范式与人类决策范式对齐——具体而言，它将模型的推理过程从基于多模态特征的直接推理转变为基于实例的类比推理过程。在三个公开数据集上的大量实验结果验证了所提方法的有效性。我们的代码可在以下链接获取：https://github.com/li-yiheng/RAMM