Recently, DNA-inspired online behavioral modeling and analysis techniques have been proposed and successfully applied to a broad range of tasks. In this paper, we employ a DNA-inspired technique to investigate the fundamental laws that drive the occurrence of similarities among Twitter users. The achieved results are multifold. First, we demonstrate that, despite apparently showing little to no similarities, the online behaviors of Twitter users are far from being uniformly random. Then, we perform a set of simulations to benchmark different behavioral models and to identify the models that better resemble human behaviors in Twitter. Finally, we demonstrate that the number and the extent of behavioral similarities within a group of Twitter users obey a log-normal distribution. Our results shed light on the fundamental properties that drive behaviors of groups of Twitter users, through the lenses of DNA-inspired behavioral modeling techniques. Our datasets are publicly available to the scientific community to further explore analytics of online behaviors.

We propose a mobile implementation of an e-voting protocol. We also provide a formal analysis to validate a security property of our system.