Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A machine-learning approach to Detect users' suspicious behaviour
  through the Facebook wall   Facebook represents the current de-facto choice for social media, changing
the nature of social relationships. The increasing amount of personal
information that runs through this platform publicly exposes user behaviour and
social trends, allowing aggregation of data through conventional intelligence
collection techniques such as OSINT (Open Source Intelligence). In this paper,
we propose a new method to detect and diagnose variations in overall Facebook
user psychology through Open Source Intelligence (OSINT) and machine learning
techniques. We are aggregating the spectrum of user sentiments and views by
using N-Games charts, which exhibit noticeable variations over time, validated
through long term collection. We postulate that the proposed approach can be
used by security organisations to understand and evaluate the user psychology,
then use the information to predict insider threats or prevent insider attacks.

18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio