False information detection on social media is challenging as it commonly requires tedious evidence-collecting but lacks available comparative information. Clues mined from user comments, as the wisdom of crowds, could be of considerable benefit to this task. However, it is non-trivial to capture the complex semantics from the contents and comments in consideration of their implicit correlations. Although deep neural networks have good expressive power, one major drawback is the lack of explainability. In this paper, we focus on how to learn from the post contents and related comments in social media to understand and detect the false information more effectively, with explainability. We thus propose a Quantum-probability based Signed Attention Network (QSAN) that integrates the quantum-driven text encoding and a novel signed attention mechanism in a unified framework. QSAN is not only able to distinguish important comments from the others, but also can exploit the conflicting social viewpoints in the comments to facilitate the detection. Moreover, QSAN is advantageous with its explainability in terms of transparency due to quantum physics meanings and the attention weights. Extensive experiments on real-world datasets show that our approach outperforms state-of-the-art baselines and can provide different kinds of user comments to explain why a piece of information is detected as false.

中文翻译：

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QSAN：基于量子概率的签名注意网络，用于可解释的虚假信息检测

社交媒体上的虚假信息检测具有挑战性，因为它通常需要繁琐的证据收集，但缺乏可用的比较信息。从用户评论中挖掘出的线索，作为群体的智慧，可能对这项任务有相当大的好处。然而，考虑到内容和评论的隐含相关性，从内容和评论中捕获复杂的语义并非易事。尽管深度神经网络具有良好的表达能力，但主要缺点之一是缺乏可解释性。在本文中，我们专注于如何从社交媒体中的帖子内容和相关评论中学习，以更有效地理解和检测虚假信息，并具有可解释性。因此，我们提出了一种基于量子概率的签名注意网络（QSAN），它在一个统一的框架中集成了量子驱动的文本编码和一种新颖的签名注意机制。QSAN不仅能够将重要评论与其他评论区分开来，还可以利用评论中相互冲突的社会观点来促进检测。此外，由于量子物理意义和注意力权重，QSAN 在透明度方面的可解释性具有优势。对真实世界数据集的大量实验表明，我们的方法优于最先进的基线，并且可以提供不同类型的用户评论来解释为什么一条信息被检测为错误。但也可以利用评论中的社会观点冲突来促进检测。此外，由于量子物理意义和注意力权重，QSAN 在透明度方面的可解释性具有优势。对真实世界数据集的大量实验表明，我们的方法优于最先进的基线，并且可以提供不同类型的用户评论来解释为什么一条信息被检测为错误。但也可以利用评论中的社会观点冲突来促进检测。此外，由于量子物理意义和注意力权重，QSAN 在透明度方面的可解释性具有优势。对真实世界数据集的大量实验表明，我们的方法优于最先进的基线，并且可以提供不同类型的用户评论来解释为什么一条信息被检测为错误。