利用社交媒体分享网络结构，提升虚假新闻源识别准确率
Network Information Enhances Unreliable News Domain Detection
- 构建域名共分享网络，发现低可信与高可信域名各自聚集
- 图神经网络相比传统模型提升13%-14%准确率，最高达63%
- 无需内容分析也能有效识别，适合内容不可靠场景
基于内容的虚假新闻检测日益困难，因低可信度来源模仿正规新闻，且生成式AI使伪造内容更难识别。本文提出从域级别出发，关注新闻源可靠性而非单篇文章。通过分析Telegram聊天中的网址分享模式，构建经统计验证的域名共分享网络，发现可靠性存在同质性聚集：低可信域名与高可信域名分别聚类。利用该结构，对比图神经网络（GNN）与无网络感知基线模型，使用多语言文本嵌入（内容相关特征）和传播动态（内容无关特征）。在相同特征下，GNN始终优于多层感知机，GraphSAGE表现最佳（含内容特征时准确率0.63，不含时0.53），相对基线提升13%-14%。结果表明，网络拓扑结构能系统性提升域级可靠性评估，即使内容分析不可行也有效。
原文摘要 · Abstract (English)
Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag. We ask whether network structure can improve news reliability classification, taking a domain-level approach that shifts the focus from individual articles to source reliability. From URL-sharing patterns in Telegram chats, we build a statistically validated domain co-sharing network and find assortative mixing by reliability: low-reliability domains group together, as do reliable ones. Exploiting this structure, we compare Graph Neural Networks against network-unaware baselines using both content-aware features (multilingual text embeddings) and content-agnostic features (spreading dynamics). GNNs consistently outperform Multi-Layer Perceptrons on identical features, with GraphSAGE best in both settings (accuracy 0.63 with content, 0.53 without), a 13-14% relative gain over the network-unaware baseline. Network topology thus systematically improves domain reliability assessment, and remains effective even when content analysis is infeasible.
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