Online disinformation has become one of the most severe concerns in today’s world. Recognizing disinformation timely and effectively is very hard, because the propagation process of disinformation is dynamic and complicated. The existing newest research leverage uniform time intervals to study the multi-stage propagation features of disinformation. However, uniform time intervals are unrealistic in the real world, cause the process of information propagation is not regular. In light of these facts, we propose a novel and effective framework Multi-stage Dynamic Disinformation Detection with Graph Entropy Guidance(MsDD) to better analyze multi-stage propagation patterns. Instead of traditional snapshots, we analyze the dynamic propagation network via graph entropy, which can work effectively in finding the dynamic and variable-length stages. In this way, we can explicitly learn the changing pattern of propagation stages and support timely detection even at the early stages. Based on this effective multi-stage analysis framework, we further propose a novel dynamic analysis model to model both the structural and sequential evolving features. Extensive experiments on two real-world datasets prove the superiority of our model. We open the datasets and source code at https://github.com/researchxr/MsDD.

网络虚假信息已成为当今世界最严重的问题之一。及时有效地识别虚假信息非常困难，因为虚假信息的传播过程是动态且复杂的。现有最新研究利用均匀时间间隔来研究虚假信息的多阶段传播特征。然而，统一的时间间隔在现实世界中是不现实的，因为信息传播的过程是不规则的。鉴于这些事实，我们提出了一种新颖有效的框架，即带有图熵指导（MsDD）的多阶段动态分布式信息检测（MsDD ），以更好地分析多阶段传播模式。我们不是传统的快照，而是通过图熵来分析动态传播网络，它可以有效地找到动态和可变长度的阶段。通过这种方式，我们可以明确地了解传播阶段的变化模式，并支持在早期阶段进行及时检测。基于这种有效的多阶段分析框架，我们进一步提出了一种新颖的动态分析模型来对结构和顺序演化特征进行建模。对两个现实世界数据集的大量实验证明了我们模型的优越性。我们在 https://github.com/researchxr/MsDD 上开放数据集和源代码。