Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Machine Learning of Open Source Russian Twitter Data
  Reveals Global Scope and Operational Characteristics   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.

16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin