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Facebook represents the current de-facto choice for social media, changingthe nature of social relationships. The increasing amount of personalinformation that runs through this platform publicly exposes user behaviour andsocial trends, allowing aggregation of data through conventional intelligencecollection techniques such as OSINT (Open Source Intelligence). In this paper,we propose a new method to detect and diagnose variations in overall Facebookuser psychology through Open Source Intelligence (OSINT) and machine learningtechniques. We are aggregating the spectrum of user sentiments and views byusing N-Games charts, which exhibit noticeable variations over time, validatedthrough long term collection. We postulate that the proposed approach can beused by security organisations to understand and evaluate the user psychology,then use the information to predict insider threats or prevent insider attacks.

A machine-learning approach to Detect users' suspicious behaviour through the Facebook wall