Social bots are all those social media accounts that are controlled by artificial, as opposed to human, intelligence. 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. 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 the automated nature of their activity. We compile a list of quantities to be measured, such as the propensity of users to engage in social interactions or to produce content. By using a large Twitter dataset associated with recent political events, we first separate bots and humans, then isolate their activity sessions. The goal of this paper is to study the behavioral dynamics that bots exhibit over the course of an activity session, and highlight if and how these differ from human activity signatures. Over the course of the years, bots have become more sophisticated, and to some extent capable of emulating the short-term behavior of human users. 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'. 2Information Sciences Institute, University of Southern California, Los Angeles, CA, United Statesīots, 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.

1Department of Computer Science and Information Systems, Birkbeck - University of London, London, United Kingdom.Iacopo Pozzana 1,2 * and Emilio Ferrara 2

Bot detector twitter