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

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…

• MF
  Miami Foundation
• TW
  Templeton World Charity Foundation

  Award: TWCF0209
• DA
  Defense Advanced Research Projects Agency

  Award: D17AC00005
• SS
  Social Sciences and Humanities Research Council of Canada

  Award: 430-2018-00452