Rating systems for online news and content represent a scalable mechanism to provide credibility cues to users. Systems can differ in who assigns ratings (expert reviewers, users, algorithms), what is being rated (individual articles, sources, claims), how ratings are aggregated and displayed, and how users interact with them.
Key papers
- 2026-kim-combating-fake-news-source-ratings — tests three rating mechanisms (expert, user-article, user-source) and their differential effects on believability and engagement
- crowd-sourced-fact-checking — examines crowdsourced approaches to rating and verifying claims
Related topics
- source-credibility (what rating systems attempt to communicate)
- crowdsourced-fact-checking (community-driven approaches)
- content-moderation (platform-level curation and labeling)
- confirmation-bias (how users respond to credibility signals)
