DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation

To provide accurate and explainable misinformation detection, it is often useful to take an auxiliary source (e.g., social context and knowledge base) into consideration. Existing methods use social contexts such as users' engagements as complementary information to improve detection performance and derive explanations. However, due to the lack of sufficient professional knowledge, users seldom respond to healthcare information, which makes these methods less applicable. In this work, to address these shortcomings, we propose a novel knowledge guided graph attention network for detecting health misinformation better. Our proposal, named as DETERRENT, leverages on the additional information from medical knowledge graph by propagating information along with the network, incorporates a Medical Knowledge Graph and an Article-Entity Bipartite Graph, and propagates the node embeddings through Knowledge Paths. In addition, an attention mechanism is applied to calculate the importance of entities to each article, and the knowledge guided article embeddings are used for misinformation detection. DETERRENT addresses the limitation on social contexts in the healthcare domain and is capable of providing useful explanations for the results of detection. Empirical validation using two real-world datasets demonstrated the effectiveness of DETERRENT. Comparing with the best results of eight competing methods, in terms of F1 Score, DETERRENT outperforms all methods by at least 4.78% on the diabetes dataset and 12.79% on cancer dataset.

为了提供准确且可解释的错误信息检测，考虑辅助来源（例如社会背景和知识库）通常是很有用的。现有的方法将用户参与度等社会背景作为补充信息来提高检测性能并得出解释。然而，由于缺乏足够的专业知识，用户很少对医疗保健信息做出回应，这使得这些方法不太适用。在这项工作中，为了解决这些缺陷，我们提出了一种新颖的知识引导图注意力网络，以便更好地检测健康错误信息。我们的方案名为DETERRENT，它通过在网络中传播信息来利用医学知识图谱中的附加信息，整合了医学知识图谱和文章 - 实体二分图，并通过知识路径传播节点嵌入。此外，应用了一种注意力机制来计算实体对每篇文章的重要性，并将知识引导的文章嵌入用于错误信息检测。DETERRENT解决了医疗保健领域中社会背景的局限性，并能够为检测结果提供有用的解释。使用两个真实世界数据集进行的实证验证证明了DETERRENT的有效性。与八种竞争方法的最佳结果相比，在F1分数方面，DETERRENT在糖尿病数据集上比所有方法至少高出4.78%，在癌症数据集上高出12.79%。