  The proliferation of unreliable news domains on the internet has had
wide-reaching negative impacts on society. We introduce and evaluate
interventions aimed at reducing traffic to unreliable news domains from search
engines while maintaining traffic to reliable domains. We build these
interventions on the principles of fairness (penalize sites for what is in
their control), generality (label/fact-check agnostic), targeted (increase the
cost of adversarial behavior), and scalability (works at webscale). We refine
our methods on small-scale webdata as a testbed and then generalize the
interventions to a large-scale webgraph containing 93.9M domains and 1.6B
edges. We demonstrate that our methods penalize unreliable domains far more
than reliable domains in both settings and we explore multiple avenues to
mitigate unintended effects on both the small-scale and large-scale webgraph
experiments. These results indicate the potential of our approach to reduce the
spread of misinformation and foster a more reliable online information
ecosystem. This research contributes to the development of targeted strategies
to enhance the trustworthiness and quality of search engine results, ultimately
benefiting users and the broader digital community.
