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

TL;DR

This paper introduces a machine learning-based method leveraging OSINT and sentiment analysis to detect suspicious user behavior on Facebook, aiming to help security organizations identify potential insider threats.

Contribution

It presents a novel approach combining OSINT, sentiment analysis, and N-Games charts to monitor and diagnose user psychology variations on Facebook.

Findings

Sentiment variations correlate with user behavior changes.

Long-term data collection validates the effectiveness of the approach.

Potential application in predicting insider threats.

Abstract

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.