The arm race between spambots and spambot-detectors is made of several cycles (or generations): a new wave of spambots is created (and new spam is spread), new spambot filters are derived and old spambots mutate (or evolve) to new species. Recently, with the diffusion of the adversarial learning approach, a new practice is emerging: to manipulate on purpose target samples in order to make stronger detection models.
DNA-inspired online behavioral modeling techniques have been proposed and successfully applied to a broad range of tasks. In this paper, we investigate the fundamental laws that drive the occurrence of behavioral similarities among Twitter users, employing a DNA-inspired technique. Our findings are multifold. First, we demonstrate that, despite apparently featuring little to no similarities, the online behaviors of Twitter users are far from being uniformly random. Secondly, we benchmark different behavioral models through a number of simulations.
© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma
