In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

中文翻译：

---

用于假新闻检测的知识感知多模态自适应图卷积网络

在本文中，我们专注于假新闻检测任务，旨在从大量社交媒体帖子中自动识别假新闻。迄今为止，已经提出了许多方法来检测假新闻，其中包括传统的学习方法和基于深度学习的模型。然而，存在三个挑战： (i) 如何有效地表示社交媒体帖子，因为帖子内容多样且高度复杂；(ii) 如何提出一种数据驱动的方法来增加模型处理不同语境和新闻背景下样本的灵活性；(iii) 如何充分利用帖子的附加辅助信息（背景知识和多模态信息）进行更好的表征学习。为应对上述挑战，我们提出了一种新颖的知识感知多模态自适应图卷积网络（KMAGCN），通过将文本信息、知识概念和视觉信息联合建模到一个统一的假新闻检测框架中来捕获语义表示。我们将帖子建模为图，并使用知识感知的多模态自适应图学习原理进行有效的特征学习。与现有方法相比，所提出的 KMAGCN 从三个方面解决了挑战：（1）它将帖子建模为图以捕获非连续和远程语义关系；(2) 提出了一种新的自适应图卷积网络来处理图数据的可变性；(3) 联合利用文本信息、知识概念和视觉信息进行模型学习。