  There is evidence that Russia's Internet Research Agency attempted to
interfere with the 2016 U.S. election by running fake accounts on Twitter -
often referred to as "Russian trolls". In this work, we: 1) develop machine
learning models that predict whether a Twitter account is a Russian troll
within a set of 170K control accounts; and, 2) demonstrate that it is possible
to use this model to find active accounts on Twitter still likely acting on
behalf of the Russian state. Using both behavioral and linguistic features, we
show that it is possible to distinguish between a troll and a non-troll with a
precision of 78.5% and an AUC of 98.9%, under cross-validation. Applying the
model to out-of-sample accounts still active today, we find that up to 2.6% of
top journalists' mentions are occupied by Russian trolls. These findings imply
that the Russian trolls are very likely still active today. Additional analysis
shows that they are not merely software-controlled bots, and manage their
online identities in various complex ways. Finally, we argue that if it is
possible to discover these accounts using externally - accessible data, then
the platforms - with access to a variety of private internal signals - should
succeed at similar or better rates.
