Abstract

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 \url{https://github.com/cuibenkuan/IIJIPN}.

Contributions
Third-order Text Graph Tensor (abbreviated as TTGT)Third-order Text Graph Tensor with three features is proposed to capture contextual information at different language properties. Sequential-based, syntactic-based, and semantic-based text graphs are constructed to form a text graph tensor. Text sequential features are extracted with point-wise mutual information to depict the property of local word co-occurrence language; text syntactic features are described with word dependencies to depict the language property in the rules of a formal grammar; text semantic features are represented with topics and topic-related key words to depict the language property of the meaning of word. Compared with other similar works that only incorporate independent sequential or semantic information for word embedding learning, our method has proposed Third-order Text Graph Tensor with sequential, semantic and syntactic information jointly learning of heterogeneous graphs. Besides, our work also proposes novel constructions of the text graph weight for sequential, syntactic and semantic features, and a novel method of semantic information extraction with topics and topic-related key words.
Intra-graph and Inter-graph Joint Information Propagation (abbreviated as IIJIP)To encode the heterogeneous information from multi-graphs, two levels of information propagation are proposed in IIJIP: intra-graph information propagation is firstly performed in each graph to realize homogeneous information interaction by building graph for each text property, and high-order homogeneous information interaction in each graph can be achieved by stacking propagation layer; inter-graph information propagation is then performed among text graphs to realize heterogeneous information interaction, and by connecting the nodes across the graphs, the heterogeneous information in one graph can be gradually fused into other graphs. To the best of our knowledge, it is the first time to implement high-order homogeneous information interaction in each graph firstly, and heterogeneous information interaction among graphs successively.

Code & Data
Code & Data: https://github.com/cuibenkuan/IIJIPN