  An individual's variation in writing style is often a function of both social
and personal attributes. While structured social variation has been extensively
studied, e.g., gender based variation, far less is known about how to
characterize individual styles due to their idiosyncratic nature. We introduce
a new approach to studying idiolects through a massive cross-author comparison
to identify and encode stylistic features. The neural model achieves strong
performance at authorship identification on short texts and through an
analogy-based probing task, showing that the learned representations exhibit
surprising regularities that encode qualitative and quantitative shifts of
idiolectal styles. Through text perturbation, we quantify the relative
contributions of different linguistic elements to idiolectal variation.
Furthermore, we provide a description of idiolects through measuring inter- and
intra-author variation, showing that variation in idiolects is often
distinctive yet consistent.
