  Bots, social media accounts controlled by software rather than by humans,
have recently been under the spotlight for their association with various forms
of online manipulation. To date, much work has focused on social bot detection,
but little attention has been devoted to the characterization and measurement
of the behavior and activity of bots, as opposed to humans'. Over the course of
the years, bots have become more sophisticated, and capable to reflect some
short-term behavior, emulating that of human users. The goal of this paper is
to study the behavioral dynamics that bots exhibit over the course of one
activity session, and highlight if and how these differ from human activity
signatures. By using a large Twitter dataset associated with recent political
events, we first separate bots and humans, then isolate their activity
sessions. We compile a list of quantities to be measured, like the propensity
of users to engage in social interactions or to produce content. Our analysis
highlights the presence of short-term behavioral trends in humans, which can be
associated with a cognitive origin, that are absent in bots, intuitively due to
their automated activity. These findings are finally codified to create and
evaluate a machine learning algorithm to detect activity sessions produced by
bots and humans, to allow for more nuanced bot detection strategies.
