We investigate the influence of fake and traditional fact-based news in Twitter during the 2016 US presidential election. Using a comprehensive dataset of 171 million tweets covering five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news. Contrary to traditional news, where influencers are mainly journalists or news outlets with verified Twitter accounts, e.g. @FoxNews and @CNN, the majority of fake news influencers have unverified or deleted accounts. We find that the three top influencers of fake news are @PrisonPlanet, @RealAlexJones and @zerohedge and top influencers of extremely bias news are @realDonaldTrump, @DailyCaller and @BreitbartNews. Two different news spreading mechanisms are revealed: (i) The influencers spreading center and left news Granger-cause the opinion of the Clinton supporters. (ii) Remarkably, this causality is reversed for fake news: the opinion of Trump supporters Granger-causes the dynamics of influencers spreading fake and extremely biased news.