  Social media platforms offer unprecedented opportunities for connectivity and
exchange of ideas; however, they also serve as fertile grounds for the
dissemination of disinformation. Over the years, there has been a rise in
state-sponsored campaigns aiming to spread disinformation and sway public
opinion on sensitive topics through designated accounts, known as troll
accounts. Past works on detecting accounts belonging to state-backed operations
focus on a single campaign. While campaign-specific detection techniques are
easier to build, there is no work done on developing systems that are
campaign-agnostic and offer generalized detection of troll accounts unaffected
by the biases of the specific campaign they belong to. In this paper, we
identify several strategies adopted across different state actors and present a
system that leverages them to detect accounts from previously unseen campaigns.
We study 19 state-sponsored disinformation campaigns that took place on
Twitter, originating from various countries. The strategies include sending
automated messages through popular scheduling services, retweeting and sharing
selective content and using fake versions of verified applications for pushing
content. By translating these traits into a feature set, we build a machine
learning-based classifier that can correctly identify up to 94% of accounts
from unseen campaigns. Additionally, we run our system in the wild and find
more accounts that could potentially belong to state-backed operations. We also
present case studies to highlight the similarity between the accounts found by
our system and those identified by Twitter.
