Recent social and political events, such as the 2016 US presidential election,
have been marked by a growing number of so-called ``fake news'',
i.e. fabricated information that
disseminate deceptive content, or grossly distort actual news reports, shared on
social media platforms.
While misinformation and propaganda have existed since ancient times,
their importance and influence
in the age of social media is still not clear.
Here, we characterize and compare the spread of information from websites containing fake news with the spread of information
from traditional news websites on the social media platform Twitter using
a dataset of more than 170 million tweets concerning the two main candidates of the 2016 US presidential election.
We find that 29% of the tweets linking to news outlets points to websites
containing fake or extremely biased news.
Analyzing the information diffusion networks, we find that
user diffusing fake news form more connected
and less heterogeneous networks than
users in traditional news diffusion networks.
Influencers of each news websites category are identified using
the collective influence algorithm.
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.
A Granger-causality analysis of the activity dynamics
of influencers reveals that influencers
of traditional news are driving the activity of the most part
of Twitter while fake news influencers
are, in fact, mostly following the activity
of Trump supporters.
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
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 seem to
not be controlled by a small set of users but rather
to take place in a tightly connected cluster of users that do not influence
the rest of Twitter activity.
Our results therefore suggest that fake and extremely biased news,
although present in considerable quantity, do not significantly
Twitter opinion and that traditional center and left leaning
news outlets are driving Twitter activity.