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Today's social media platforms enable to spread both authentic and fake newsvery quickly. Some approaches have been proposed to automatically detect such"fake" news based on their content, but it is difficult to agree on universalcriteria 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 notbased 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 ofnews items (the most popular ones), which enables to have an estimation of eachuser's inclination to share fake news items. Then, using a Bayesian approach,we estimate the trustworthiness of future news items, and treat accordinglythose of them that pass a certain "untrustworthiness" threshold.  We then evaluate the effectiveness and overhead of this technique on a largeTwitter graph. We show that having a few thousands users exposed to one givennews item enables to reach a very precise estimation of its reliability. Wethus identify more than 99% of fake news items with no false positives. Theperformance impact is very small: the induced overhead on the 90th percentilelatency is less than 3%, and less than 8% on the throughput of user operations.

Limiting the Spread of Fake News on Social Media Platforms by Evaluating Users' Trustworthiness