Title: Unsupervised Machine Learning of Open Source Russian Twitter Data Reveals Global Scope and Operational Characteristics,
Abstract: We developed and used a collection of statistical methods (unsupervised
machine learning) to extract relevant information from a Twitter supplied data
set consisting of alleged Russian trolls who (allegedly) attempted to influence
the 2016 US Presidential election. These unsupervised statistical methods allow
fast identification of (i) emergent language communities within the troll
population, (ii) the transnational scope of the operation and (iii) operational
characteristics of trolls that can be used for future identification. Using
natural language processing, manifold learning and Fourier analysis, we
identify an operation that includes not only the 2016 US election, but also the
French National and both local and national German elections. We show the
resulting troll population is composed of users with common, but clearly
customized, behavioral characteristics.