Title: Influence of fake news in Twitter during the 2016 US presidential election

To understand the role of fake news in Twitter during the 2016 US election, we combined machine learning, network science, statistical physics and causality analysis to not only measure the importance of fake news compared to traditional news in Twitter but also understand their influence and the mechanisms of their diffusion. Using a dataset of more than 170 million tweets covering the five months preceding election day and concerning the two main candidates of the 2016 US presidential election, we find that 25% of the tweets linking to a news spread either fake or extremely biased news.

We analyzed the networks of information flow and found the most important news spreaders by using the theory of optimal percolation and used a multivariate causal network reconstruction to uncover how fake news influenced the presidential election. While influencers of traditional news outlets are journalists and public figures with verified Twitter accounts, most influencers of fake news and extremely biased websites are unknown users or users with deleted Twitter accounts. 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.

Our investigation provides new insights into the dynamics of news diffusion in Twitter. Namely, our results suggests that fake and extremely biased news are governed by a different diffusion mechanism than traditional center and left leaning news. Center and left leaning news diffusion is driven by a small number of influential users, mainly journalists, and follow a diffusion cascade in a network with heterogeneous degree distribution which is typical of diffusion in social networks, while the diffusion of fake and extremely biased news seems to not be controlled by a small set of influencers but rather to take place in more connected clusters and to be the result of a collective behavior.