  Social media, once hailed as a vehicle for democratization and the promotion
of positive social change across the globe, are under attack for becoming a
tool of political manipulation and spread of disinformation. A case in point is
the alleged use of trolls by Russia to spread malicious content in Western
elections. This paper examines the Russian interference campaign in the 2016 US
presidential election on Twitter. Our aim is twofold: first, we test whether
predicting users who spread trolls' content is feasible in order to gain
insight on how to contain their influence in the future; second, we identify
features that are most predictive of users who either intentionally or
unintentionally play a vital role in spreading this malicious content. We
collected a dataset with over 43 million elections-related posts shared on
Twitter between September 16 and November 9, 2016, by about 5.7 million users.
This dataset includes accounts associated with the Russian trolls identified by
the US Congress. Proposed models are able to very accurately identify users who
spread the trolls' content (average AUC score of 96%, using 10-fold
validation). We show that political ideology, bot likelihood scores, and some
activity-related account meta data are the most predictive features of whether
a user spreads trolls' content or not.
