Cui Benkuan, Ma Kun, Li Leping, Zhang Weijuan, Ji Ke, Chen Zhenxiang, Abraham Ajith
School of Information Science and Engineering, University of Jinan, Jinan, 250022 China.
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022 China.
Appl Intell (Dordr). 2023 Feb 15:1-18. doi: 10.1007/s10489-023-04455-1.
Although the Internet and social media provide people with a range of opportunities and benefits in a variety of ways, the proliferation of fake news has negatively affected society and individuals. Many efforts have been invested to detect the fake news. However, to learn the representation of fake news by context information, it has brought many challenges for fake news detection due to the feature sparsity and ineffectively capturing the non-consecutive and long-range context. In this paper, we have proposed Intra-graph and Inter-graph Joint Information Propagation Network (abbreviated as IIJIPN) with Third-order Text Graph Tensor for fake news detection. Specifically, data augmentation is firstly utilized to solve the data imbalance and strengthen the small corpus. In the stage of feature extraction, Third-order Text Graph Tensor with sequential, syntactic, and semantic features is proposed to describe contextual information at different language properties. After constructing the text graphs for each text feature, Intra-graph and Inter-graph Joint Information Propagation is used for encoding the text: intra-graph information propagation is performed in each graph to realize homogeneous information interaction, and high-order homogeneous information interaction in each graph can be achieved by stacking propagation layer; inter-graph information propagation is performed among text graphs to realize heterogeneous information interaction by connecting the nodes across the graphs. Finally, news representations are generated by attention mechanism consisting of graph-level attention and node-level attention mechanism, and then news representations are fed into a fake news classifier. The experimental results on four public datasets indicate that our model has outperformed state-of-the-art methods. Our source code is available at https://github.com/cuibenkuan/IIJIPN.
尽管互联网和社交媒体以多种方式为人们提供了一系列机会和益处，但假新闻的泛滥对社会和个人产生了负面影响。人们已经投入了许多努力来检测假新闻。然而，要通过上下文信息了解假新闻的表征，由于特征稀疏以及难以有效捕捉非连续和长距离上下文，这给假新闻检测带来了诸多挑战。在本文中，我们提出了用于假新闻检测的带三阶文本图张量的图内和图间联合信息传播网络（简称为IIJIPN）。具体而言，首先利用数据增强来解决数据不平衡问题并强化小语料库。在特征提取阶段，提出了具有顺序、句法和语义特征的三阶文本图张量来描述不同语言属性的上下文信息。在为每个文本特征构建文本图之后，使用图内和图间联合信息传播对文本进行编码：在每个图中执行图内信息传播以实现同质信息交互，并且通过堆叠传播层可以在每个图中实现高阶同质信息交互；在文本图之间执行图间信息传播，通过跨图连接节点来实现异质信息交互。最后，通过由图级注意力和节点级注意力机制组成的注意力机制生成新闻表征，然后将新闻表征输入到假新闻分类器中。在四个公共数据集上的实验结果表明，我们的模型优于现有方法。我们的源代码可在https://github.com/cuibenkuan/IIJIPN获取。
