Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in the spreading chain. In this study, we propose a novel participant-level rumor detection framework. It explicitly models and integrates various fine-grained user representations (i.e., user influence, susceptibility, and temporal information) of all participants from the propagation threads via deep representation learning. Experiments conducted on real-world datasets demonstrate a significant accuracy improvement of our approach. Theoretically, we contribute to the effective usage of data science and analytics for social information diffusion design, particularly rumor detection. Practically, our results can be used to improve the quality of rumor detection services for social platforms.

研究人员在设计自动检测和识别谣言的方法方面付出了巨大的努力。传统方法侧重于特征工程。它们需要大量的人工操作，并且难以概括。深度学习解决方案来帮忙。然而，他们通常无法捕捉到谣言传播的底层结构以及传播链中所有参与者的影响。在这项研究中，我们提出了一种新颖的参与者级谣言检测框架。它通过深度表征学习对传播线程中所有参与者的各种细粒度用户表征（即用户影响力、敏感性和时间信息）进行显式建模和集成。在真实世界数据集上进行的实验证明了我们方法的显着准确性改进。理论上，我们致力于有效利用数据科学和分析进行社会信息传播设计，尤其是谣言检测。实际上，我们的结果可用于提高社交平台谣言检测服务的质量。