Misinformation Resilient Search Rankings with Webgraph-based Interventions

TL;DR

This paper proposes webgraph-based interventions to reduce traffic to unreliable news domains from search engines, aiming to improve online information quality while maintaining access to reliable sources.

Contribution

It introduces scalable, fair, and generalizable methods to penalize unreliable domains, tested on both small and large webgraphs with promising results.

Findings

Unreliable domains are penalized more than reliable ones.

Methods are effective at both small-scale and large-scale webgraphs.

Potential to reduce misinformation spread online.

Abstract

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.