  The dynamics and influence of fake news on Twitter during the 2016 US
presidential election remains to be clarified. Here, we use a dataset of 171
million tweets in the five months preceding the election day to identify 30
million tweets, from 2.2 million users, which contain a link to news outlets.
Based on a classification of news outlets curated by www.opensources.co, we
find that 25% of these tweets spread either fake or extremely biased news. We
characterize the networks of information flow to find the most influential
spreaders of fake and traditional news and use causal modeling to uncover how
fake news influenced the presidential election. We find that, while top
influencers spreading traditional center and left leaning news largely
influence the activity of Clinton supporters, this causality is reversed for
the fake news: the activity of Trump supporters influences the dynamics of the
top fake news spreaders.
