  Author similarity and detection is an integral first step in detecting
state-led disinformation campaigns in an automated fashion. Current detection
techniques require an analyst or subject matter expert to hand-curate accounts.
Stylometric features have a rich history in identifying authorship of unknown
documents, but little exploration has been done to compare authors to one
another. We have adapted a select handful of stylometric features for use in
author similarity metrics, and show their >0.96 F-1 performance on a curated
author classification task, across both traditional machine learning and deep
learning models. These features should contribute to the expanding field of
author similarity research, and expedite the process of detecting and
mitigating large-scale social media disinformation campaigns.
