  Social networks offer a ready channel for fake and misleading news to spread
and exert influence. This paper examines the performance of different
reputation algorithms when applied to a large and statistically significant
portion of the news that are spread via Twitter. Our main result is that simple
crowdsourcing-based algorithms are able to identify a large portion of fake or
misleading news, while incurring only very low false positive rates for
mainstream websites. We believe that these algorithms can be used as the basis
of practical, large-scale systems for indicating to consumers which news sites
deserve careful scrutiny and skepticism.
