计算机科学
图形
嵌入
知识图
关系(数据库)
文字嵌入
假新闻
卷积神经网络
情报检索
元组
理论计算机科学
人工智能
数据挖掘
互联网隐私
数学
离散数学
作者
Kun Wu,Xu Yuan,Yue Ning
标识
DOI:10.1007/978-3-030-75768-7_32
摘要
The greater public has become aware of the rising prevalence of untrustworthy information in online media. Extensive adaptive detection methods have been proposed for mitigating the adverse effect of fake news. Computational methods for detecting fake news based on the news content have several limitations, such as: 1) Encoding semantics from original texts is limited to the structure of the language in the text, making both bag-of-words and embedding-based features deceptive in the representation of a fake news, and 2) Explainable methods often neglect relational contexts in fake news detection. In this paper, we design a knowledge graph enhanced framework for effectively detecting fake news while providing relational explanation. We first build a credential-based multi-relation knowledge graph by extracting entity relation tuples from our training data and then apply a compositional graph convolutional network to learn the node and relation embeddings accordingly. The pre-trained graph embeddings are then incorporated into a graph convolutional network for fake news detection. Through extensive experiments on three real-world datasets, we demonstrate the proposed knowledge graph enhanced framework has significant improvement in terms of fake news detection as well as structured explainability.
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