  Website reliability labels underpin almost all research in misinformation
detection. However, misinformation sources often exhibit transient behavior,
which makes many such labeled lists obsolete over time. We demonstrate that
Search Engine Optimization (SEO) attributes provide strong signals for
predicting news site reliability. We introduce a novel attributed webgraph
dataset with labeled news domains and their connections to outlinking and
backlinking domains. We demonstrate the success of graph neural networks in
detecting news site reliability using these attributed webgraphs, and show that
our baseline news site reliability classifier outperforms current SoTA methods
on the PoliticalNews dataset, achieving an F1 score of 0.96. Finally, we
introduce and evaluate a novel graph-based algorithm for discovering previously
unknown misinformation news sources.
