With the rise of online social networks, detecting fake news accurately is essential for a healthy online environment. While existing methods have advanced multimodal fake news detection, they often neglect the multi-view visual-semantic aspects of news, such as different text perspectives of the same image. To address this, we propose a Multi-View Visual-Semantic Representation (MViR) framework. Our approach includes a Multi-View Representation module using pyramid dilated convolution to capture multi-view visual-semantic features, a Multi-View Feature Fusion module to integrate these features with text, and multiple aggregators to extract multi-view semantic cues for detection. Experiments on benchmark datasets demonstrate the superiority of MViR. The source code of FedCoop is available at https://github.com/FlowerinZDF/FakeNews-MVIR.

翻译：随着在线社交网络的兴起，准确检测虚假新闻对于构建健康的网络环境至关重要。尽管现有方法在多模态虚假新闻检测方面取得了进展，但它们往往忽略了新闻的多视角视觉-语义特性，例如同一图像的不同文本视角。为解决这一问题，我们提出了一个多视角视觉-语义表征（MViR）框架。该方法包含一个使用金字塔空洞卷积捕捉多视角视觉-语义特征的多视角表征模块、一个将这些特征与文本融合的多视角特征融合模块，以及多个用于提取多视角语义线索以进行检测的聚合器。在基准数据集上的实验证明了MViR的优越性。FedCoop的源代码可在 https://github.com/FlowerinZDF/FakeNews-MVIR 获取。