In an attempt to mimic the complex paths through which unreliable content
spreads between search engines and social media, we explore the impact of
incorporating both webgraph and large-scale social media contexts into website
credibility classification and discovery systems. We further explore the usage
of what we define as \textit{dredge words} on social media -- terms or phrases
for which unreliable domains rank highly. Through comprehensive graph neural
network ablations, we demonstrate that curriculum-based heterogeneous graph
models that leverage context from both webgraphs and social media data
outperform homogeneous and single-mode approaches. We further demonstrate that
the incorporation of dredge words into our model strongly associates unreliable
websites with social media and online commerce platforms. Finally, we show our
heterogeneous model greatly outperforms competing systems in the top-k
identification of unlabeled unreliable websites. We demonstrate the strong
unreliability signals present in the diverse paths that users follow to uncover
unreliable content, and we release a novel dataset of dredge words.

通过综合使用网络图和大规模社交媒体语境，我们探讨了将 Web 图和社交媒体上下文纳入网站可信度分类和发现系统的影响。同时，通过学术实验，我们证明了利用多层异构图神经网络模型，结合 Web 图和社交媒体数据的上下文，可以优于单一模式方法。该研究还展示了在我们的模型中加入 dredge words（指与不可靠域高排名相关的术语或短语）可以将不可靠网站与社交媒体和在线商务平台强相关联。最终，我们的异构模型在无标签不可靠网站的 top-k 识别中远超竞争系统。我们的研究揭示了用户发现不可靠内容的多样路径中存在强烈的不可靠性信号，并发布了一份新颖的 dredge words 数据集。