In recent years, the proliferation of misinformation and fake news has posed serious threats to individuals and society, spurring intense research into automated detection methods. Previous work showed that integrating content, user preferences, and propagation structure achieves strong performance, but leaves all graph-level representation learning entirely to the GNN, hiding any explicit topological cues. To close this gap, we introduce a lightweight enhancement: for each node, we append two classical graph-theoretic metrics, degree centrality and local clustering coefficient, to its original BERT and profile embeddings, thus explicitly flagging the roles of hub and community. In the UPFD Politifact subset, this simple modification boosts macro F1 from 0.7753 to 0.8344 over the original baseline. Our study not only demonstrates the practical value of explicit topology features in fake-news detection but also provides an interpretable, easily reproducible template for fusing graph metrics in other information-diffusion tasks.

翻译：近年来，虚假信息和虚假新闻的泛滥对个人和社会构成了严重威胁，推动了自动化检测方法的深入研究。先前研究表明，整合内容特征、用户偏好与传播结构可获得优异性能，但将全部图级表征学习完全交由图神经网络处理，从而掩盖了显式的拓扑线索。为弥补这一不足，我们提出一种轻量级增强方案：为每个节点在其原始BERT嵌入和用户画像嵌入的基础上，附加两个经典图论度量指标——度中心性与局部聚类系数，从而显式标注其枢纽节点与社区成员的角色。在UPFD Politifact子集上的实验表明，这一简单改进将宏观F1分数从基线模型的0.7753提升至0.8344。本研究不仅证实了显式拓扑特征在虚假新闻检测中的实用价值，更为其他信息传播任务中融合图度量指标提供了可解释、易复现的参考范式。