  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.
