用节点拓扑特征提升假新闻检测准确率
Enhancing Fake-News Detection with Node-Level Topological Features
- 在BERT嵌入中加入度中心性和局部聚类系数
- 在Politifact数据集上宏平均F1提升至0.8344
- 方法简单可解释，适用于各类信息传播任务
近年来，虚假信息的泛滥对个人和社会构成严重威胁，推动了自动化检测方法的研究。已有研究证明，结合内容、用户偏好和传播结构能取得良好效果，但图神经网络完全负责图级别表征学习，隐藏了显式的拓扑线索。为弥补这一缺口，我们提出一种轻量级增强：对每个节点，将其原始BERT嵌入和用户画像嵌入与两个经典图论指标——度中心性与局部聚类系数拼接，从而明确标识出枢纽节点和社区角色。在UPFD Politifact子集上，该简单修改使宏平均F1从0.7753提升至0.8344。本研究不仅验证了显式拓扑特征在假新闻检测中的实际价值，还为其他信息扩散任务提供了可解释、易复现的图度量融合模板。
原文摘要 · Abstract (English)
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.
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