Unsupervised Machine Learning of Open Source Russian Twitter Data Reveals Global Scope and Operational Characteristics

TL;DR

This paper employs unsupervised machine learning techniques on Russian Twitter data to uncover the global scope, language communities, and operational traits of troll operations influencing multiple elections.

Contribution

It introduces a novel combination of statistical methods for analyzing troll behavior and scope, revealing transnational influence beyond the 2016 US election.

Findings

Identified language communities within troll populations

Revealed transnational scope of troll operations

Characterized operational behaviors of trolls

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.