Business

Ranking (information retrieval)

Intervention (counseling)

Mathematics

News media

Internet privacy

Advertising

Epistemology

World Wide Web

Psychiatry

Mathematical analysis

Philosophy

Psychology

Information retrieval

Misinformation

Quality (philosophy)

Fake news

Content (measure theory)

Computer science

Social media

Computer security

Social psychology

Proceedings of the National Academy of Sciences

Significance Many people consume news via social media. It is therefore desirable to reduce social media users’ exposure to low-quality news content. One possible intervention is for social media ranking algorithms to show relatively less content from sources that users deem to be untrustworthy. But are laypeople’s judgments reliable indicators of quality, or are they corrupted by either partisan bias or lack of information? Perhaps surprisingly, we find that laypeople—on average—are quite good at distinguishing between lower- and higher-quality sources. These results indicate that incorporating the trust ratings of laypeople into social media ranking algorithms may prove an effective intervention against misinformation, fake news, and news content with heavy political bias.

Fighting misinformation on social media using crowdsourced judgments of news source quality