  Today's social media platforms enable to spread both authentic and fake news
very quickly. Some approaches have been proposed to automatically detect such
"fake" news based on their content, but it is difficult to agree on universal
criteria of authenticity (which can be bypassed by adversaries once known).
Besides, it is obviously impossible to have each news item checked by a human.
  In this paper, we a mechanism to limit the spread of fake news which is not
based on content. It can be implemented as a plugin on a social media platform.
The principle is as follows: a team of fact-checkers reviews a small number of
news items (the most popular ones), which enables to have an estimation of each
user's inclination to share fake news items. Then, using a Bayesian approach,
we estimate the trustworthiness of future news items, and treat accordingly
those of them that pass a certain "untrustworthiness" threshold.
  We then evaluate the effectiveness and overhead of this technique on a large
Twitter graph. We show that having a few thousands users exposed to one given
news item enables to reach a very precise estimation of its reliability. We
thus identify more than 99% of fake news items with no false positives. The
performance impact is very small: the induced overhead on the 90th percentile
latency is less than 3%, and less than 8% on the throughput of user operations.
