Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021)

Abstract: Nowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing methods rely heavily on linguistic and semantic features from the news content, and fail to effectively exploit external knowledge which could help determine whether the news document is trusted. In this paper, we propose a novel end-to-end graph neural model called CompareNet, which compares the news to the knowledge base (KB) through entities for fake news detection. Considering that fake news detection is correlated with topics, we also incorporate topics to enrich the news representation. Specifically, we first construct a directed heterogeneous document graph for each news incorporating topics and entities. Based on the graph, we develop a heterogeneous graph attention network for learning the topic-enriched news representation as well as the contextual entity representations that encode the semantics of the news content. The contextual entity representations are then compared to the corresponding KB-based entity representations through a carefully designed entity comparison network , to capture the consistency between the news content and KB. Finally, the topic-enriched news representation combining the entity comparison features is fed into a fake news classifier. Experimental results on two benchmark datasets demonstrate that CompareNet significantly outperforms state-of-the-art methods.

## introduction with the rapid development of the internet, there are increasingly huge opportunities for fake news * the work was done while visiting micorosft research asia. some existing fake news detection methods rely heavily on various hand-crafted linguistic and semantic features for differentiating between news documents @xcite @xcite @xcite . to avoid feature engineering, deep neural models such as bi-lstm and convolutional neural networks (cnn) have been employed @xcite @xcite . however, they fail to consider the sentence interactions in the document. @xcite showed that trusted news and fake news have different patterns of sentence interactions @xcite . they modeled a news document as a fully connected sentence graph and proposed a graph attention model for fake news detection. although these existing approaches can be effective, they fail to fully exploit external kb which could help determine whether the news is fake or trusted. external kb such as wikipedia contains a large amount of high-quality structured subjectpredicate-object triplets and unstructured entity descriptions, which could serve as evidence for detecting fake news. as shown in figure 4 , the news document about "mammograms are not effective at detecting breast tumors" is likely to be detected as fake news with the knowledge that " the goal of mammography is the early detection of breast cancer" in the wikipedia entity description page 1 . pan et al. proposed to construct knowledge graphs from positive and negative news, and apply transe to learn triplet scores for fake news detection @xcite . nevertheless, the performance is largely influenced by construction of the knowledge graph. in this paper, to take full advantage of the external knowledge, we propose a novel endto-end graph neural model comparenet which directly compares the news to the kb through entities for fake news detection. in comparenet, we also consider using topics to enrich the news document representation for improving fake news detection, since fake news detection and topics are highly correlated @xcite . for example, the news documents in the "health" topic are inclined towards false, while the documents belonging to the "economy" topic are biased to be trusted instead. particularly, we first construct a directed heterogeneous document graph for each news document, containing sentences, topics and entities as nodes.the sentences are fully connected in bidirection. each sentence is also connected with its top relevant topics in bi-direction. if a sentence contains an entity, one directed link is built from the sentence to the entity. the reason for building one-way links from sentences to entities is to ensure that we can learn contextual entity representations that encode the semantics of the news, while avoiding the influence of the true entity knowledge to the news representation. based on the directed heterogeneous document graph, we develop a heterogeneous graph attention network to learn topic-enriched news representations and contextual entity representations. the learned contextual entity representations are then compared to the corresponding kb-based entity representations with a carefully designed entity comparison network, in order to capture the semantic consistency between the news content and external kb. finally, the topic-enriched news representations and the entity comparison features are combined for fake news classification. to facilitate related researches, we release both our code and dataset to the public 2 . fake news detection has attracted much attention in recent years @xcite . a lot of works also focus on the related problem, i.e., fact checking, which aims to search evidence from external knowledge to verify the veracity of a claim (e.g., a subject-predicateobject triple) @xcite @xcite . generally, fake news detection usually focuses on news events while fact-checking is broader @xcite . the approaches for fake news detection can be divided into two categories: social-based and content-based. ## our proposed comparenet in this section, we detail our proposed fake news detection model comparenet, which directly compares the news to external knowledge for fake news detection. as shown in figure 2 , we also consider topics for enriching news representation since fake news detection is highly correlated with topics @xcite . specifically, we first construct a directed heterogeneous document graph for each news document incorporating topics and entities as shown in figure 1 . the graph well captures the interactions among sentences, topics and entities. based on the graph, we develop a heterogeneous graph attention network to learn the topic-enriched news representation as well as the contextual entity representations that encode the semantics of the news document. to fully leverage external kb, we take the entities as the bridge between the news document and the kb. we compare the contextual entity representations with the corresponding kb-based entity representations using a carefully designed entity comparison network. finally, the obtained entity comparison features are combined with the topic-enriched news document representation for fake news detection. ## experiments we conduct extensive experiments across various settings and datasets. following the previous work @xcite , we use sln: satirical and legitimate news database @xcite , and lun: labeled unreliable news dataset @xcite for our experiments. ## conclusion in this paper, we propose a novel end-to-end graph neural model comparenet which compares the news to the external knowledge for fake news detection. considering that the detection of fake news is correlated with topics, in our model, we also use topics to enrich the news document representation for improving fake news detection. particularly, we first construct a directed heterogeneous document graph for each news document capturing the interactions among sentences, topics and entities. based on the graph, we develop a heterogeneous graph attention network for learning topic-enriched news representation as well as contextual entity representations that encode the semantics of the content of the news document. to capture the semantic consistency of the news content and the kb, the learned contextual entity representations are then compared to the kb-based entity representations, with a carefully designed entity comparison network. finally, the obtained entity comparison features are combined with the news representation for an improved fake news classifier. experiments on two benchmark datasets have demonstrated the effectiveness of the way we incorporate the external knowledge and topics. in future work, we will explore a better way to combine multi-modal data (e.g., images) and external knowledge for fake news detection.