  Many natural language related applications involve text generation, created
by humans or machines. While in many of those applications machines support
humans, yet in few others, (e.g. adversarial machine learning, social bots and
trolls) machines try to impersonate humans. In this scope, we proposed and
evaluated several mutation-based text generation approaches. Unlike
machine-based generated text, mutation-based generated text needs human text
samples as inputs. We showed examples of mutation operators but this work can
be extended in many aspects such as proposing new text-based mutation operators
based on the nature of the application.
