Incorporating Relational Knowledge in Explainable Fake News Detection
Incorporating Relational Knowledge in Explainable Fake News Detection
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DOI：
10.1007/978-3-030-75768-7_32
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Kun Wu;Xu Yuan;Yue Ning
中科院分区：
文献类型：
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作者：
Kun Wu;Xu Yuan;Yue Ning
更多的公众已经意识到网络媒体上不可信的信息越来越普遍。已经提出了广泛的自适应检测方法来减轻假新闻的不利影响。基于新闻内容检测假新闻的计算方法有几个局限性，例如：1)来自原始文本的编码语义仅限于文本中的语言结构，使得词袋和基于嵌入的特征在假新闻的表示中具有欺骗性；2)可解释方法在假新闻检测中经常忽略关系上下文。在本文中，我们设计了一个知识图谱增强框架，在提供关联解释的同时有效地检测假新闻。首先从训练数据中提取实体关系元组，构建基于凭证的多关系知识图，然后应用组合图卷积网络学习节点和关系嵌入。然后将预训练的图嵌入合并到图卷积网络中用于假新闻检测。通过对三个真实数据集的广泛实验，我们证明了所提出的知识图增强框架在假新闻检测和结构化可解释性方面有显着改进。
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.