Existing rumor detection strategies typically provide detection labels while ignoring their explanation. Nonetheless, providing pieces of evidence to explain why a suspicious tweet is rumor is essential. As such, a novel model, LOSIRD, was proposed in this paper. First, LOSIRD mines appropriate evidence sentences and classifies them by automatically checking the veracity of the relationship of the given claim and its evidence from about 5 million Wikipedia documents. LOSIRD then automatically constructs two heterogeneous graph objects to simulate the propagation layout of the tweets and code the rela?tionship of evidence. Finally, a graphSAGE processing component is used in LOSIRD to provide the label and evidence. To the best of our knowledge, we are the first one who combines objective facts and subjective views to verify rumor. The experimental results on two real-world Twitter datasets showed that our model exhibited the best performance in the early rumor detection task and its rumor detection performance outperformed other baseline and state-of-the-art models. Moreover, we confirmed that both objective information and subjective information are fundamental clues for rumor detection.

中文翻译：

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认识真相：利用客观事实和主观观点进行可解释的谣言检测

现有的谣言检测策略通常会提供检测标签，而忽略它们的解释。尽管如此，提供证据来解释为什么可疑的推文是谣言是必不可少的。因此，本文提出了一种新颖的模型 LOSIRD。首先，LOSIRD 从大约 500 万份维基百科文档中挖掘适当的证据句子，并通过自动检查给定声明与其证据之间关系的真实性对它们进行分类。LOSIRD 然后自动构建两个异构图对象来模拟推文的传播布局并编码证据的关系。最后，在 LOSIRD 中使用了一个 graphSAGE 处理组件来提供标签和证据。据我们所知，我们是第一个结合客观事实和主观观点来验证谣言的人。在两个真实世界 Twitter 数据集上的实验结果表明，我们的模型在早期谣言检测任务中表现出最佳性能，其谣言检测性能优于其他基线和最新模型。此外，我们确认客观信息和主观信息都是谣言检测的基本线索。