Fake news detection has received increasing attention from researchers in recent years, especially multi-modal fake news detection containing both text and images.However, many previous works have fed two modal features, text and image, into a binary classifier after a simple concatenation or attention mechanism, in which the features contain a large amount of noise inherent in the data,which in turn leads to intra- and inter-modal uncertainty.In addition, although many methods based on simply splicing two modalities have achieved more prominent results, these methods ignore the drawback of holding fixed weights across modalities, which would lead to some features with higher impact factors being ignored.To alleviate the above problems, we propose a new dynamic fusion framework dubbed MDF for fake news detection.As far as we know, it is the first attempt of dynamic fusion framework in the field of fake news detection.Specifically, our model consists of two main components:(1) UEM as an uncertainty modeling module employing a multi-head attention mechanism to model intra-modal uncertainty; and (2) DFN is a dynamic fusion module based on D-S evidence theory for dynamically fusing the weights of two modalities, text and image.In order to present better results for the dynamic fusion framework, we use GAT for inter-modal uncertainty and weight modeling before DFN.Extensive experiments on two benchmark datasets demonstrate the effectiveness and superior performance of the MDF framework.We also conducted a systematic ablation study to gain insight into our motivation and architectural design.We make our model publicly available to:https://github.com/CoisiniStar/MDF

翻译：近年来，虚假新闻检测日益受到研究者的关注，尤其是包含文本和图像的多模态虚假新闻检测。然而，先前许多研究在将文本和图像两种模态特征进行简单拼接或注意力机制处理后，便直接输入二元分类器，这些特征中包含了数据固有的大量噪声，进而导致了模态内与模态间的不确定性。此外，尽管许多基于简单拼接双模态的方法取得了较为突出的结果，但这些方法忽视了跨模态固定权重分配的缺陷，这会导致某些具有更高影响因子的特征被忽略。为缓解上述问题，我们提出了一种名为MDF的新型动态融合框架用于虚假新闻检测。据我们所知，这是动态融合框架在虚假新闻检测领域的首次尝试。具体而言，我们的模型包含两个主要组件：(1) UEM作为不确定性建模模块，采用多头注意力机制来建模模态内不确定性；(2) DFN是基于D-S证据理论的动态融合模块，用于动态融合文本和图像两种模态的权重。为使动态融合框架呈现更优结果，我们在DFN之前使用GAT进行模态间不确定性与权重建模。在两个基准数据集上的大量实验证明了MDF框架的有效性与优越性能。我们还进行了系统的消融研究，以深入理解我们的动机与架构设计。我们的模型已公开于：https://github.com/CoisiniStar/MDF