Emergent properties, models, and laws of behavioral similarities within groups of twitter users

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. We characterize the main properties of such models and we identify those models that better resemble human behaviors in Twitter. Then, we demonstrate that the number and the extent of behavioral similarities within a group of Twitter users obey a log-normal law, and we leverage this characterization to propose a novel bot detection system. In a nutshell, the results shed light on the fundamental properties that drive the online behaviors of groups of Twitter users, through the lenses of DNA-inspired behavioral modeling techniques. This study is based on a wealth of data gathered over several months that, for the sake of reproducibility, are publicly available for research purposes.