  Recently, Web forums have been invaded by opinion manipulation trolls. Some
trolls try to influence the other users driven by their own convictions, while
in other cases they can be organized and paid, e.g., by a political party or a
PR agency that gives them specific instructions what to write. Finding paid
trolls automatically using machine learning is a hard task, as there is no
enough training data to train a classifier; yet some test data is possible to
obtain, as these trolls are sometimes caught and widely exposed. In this paper,
we solve the training data problem by assuming that a user who is called a
troll by several different people is likely to be such, and one who has never
been called a troll is unlikely to be such. We compare the profiles of (i) paid
trolls vs. (ii)"mentioned" trolls vs. (iii) non-trolls, and we further show
that a classifier trained to distinguish (ii) from (iii) does quite well also
at telling apart (i) from (iii).
