At present, rumors are growing wantonly with the convenience and influence of social media, becoming a problem that may severely impact social stability and development. The rumor is not an objective judgment but a process of multi-dimensional subjective value superposition and a collective transaction of people’s thoughts on social networks. How to fully mine the critical features of rumor detection from the complex information of social networks is a challenge to the existing rumor detection models. Therefore, we present the explainable model GMIN (Graph-aware Multi-feature Interacting Network), aiming to fully exploit the multifaceted features of social networks by deeply analyzing the mechanism of rumor propagation and the nature of social networks. GMIN incorporates four modules to capture: the characteristics of people receiving and spreading information, the interactions and latent associations in social networks, the mechanisms of rumor propagation and diffusion, and the collaborations between various features. It is worth noting that GMIN is interpretable in the results and the model’s components. The experimental results on three real-world datasets demonstrate the validity of our proposed model and show excellent capabilities in early rumor detection and the interpretability of the model.

中文翻译：

---

图感知多特征交互网络，用于社交网络上可解释的谣言检测

当前，借助社交媒体的便利性和影响力，谣言肆意滋生，成为严重影响社会稳定和发展的问题。谣言不是客观判断，而是多维主观价值叠加的过程，是人们在社交网络上的集体思想交易。如何从社交网络复杂的信息中充分挖掘谣言检测的关键特征是对现有谣言检测模型的挑战。因此，我们提出了可解释的模型GMIN（Graph-aware Multi-feature Interacting Network），旨在通过深入分析谣言传播机制和社交网络的本质，充分利用社交网络的多方面特征。 GMIN包含四个模块来捕获：人们接收和传播信息的特征、社交网络中的交互和潜在关联、谣言传播和扩散的机制以及各种特征之间的协作。值得注意的是，GMIN 在结果和模型组件中是可解释的。三个真实世界数据集的实验结果证明了我们提出的模型的有效性，并显示出早期谣言检测和模型可解释性的出色能力。