  Online Social Networks have revolutionized how we consume and share
information, but they have also led to a proliferation of content not always
reliable and accurate. One particular type of social accounts is known to
promote unreputable content, hyperpartisan, and propagandistic information.
They are automated accounts, commonly called bots. Focusing on Twitter
accounts, we propose a novel approach to bot detection: we first propose a new
algorithm that transforms the sequence of actions that an account performs into
an image; then, we leverage the strength of Convolutional Neural Networks to
proceed with image classification. We compare our performances with
state-of-the-art results for bot detection on genuine accounts / bot accounts
datasets well known in the literature. The results confirm the effectiveness of
the proposal, because the detection capability is on par with the state of the
art, if not better in some cases.
