  Internet users generate content at unprecedented rates. Building intelligent
systems capable of discriminating useful content within this ocean of
information is thus becoming a urgent need. In this paper, we aim to predict
the usefulness of Amazon reviews, and to do this we exploit features coming
from an off-the-shelf argumentation mining system. We argue that the usefulness
of a review, in fact, is strictly related to its argumentative content, whereas
the use of an already trained system avoids the costly need of relabeling a
novel dataset. Results obtained on a large publicly available corpus support
this hypothesis.
