Unveiling Ideological Trends through Data Analytics to Construe National Security Instabilities

In this paper, a methodology to disclose ideological features using data analytics techniques aimed at interpreting national security instabilities is proposed. The analysis is based on two concepts, namely, authoritarianism and an attribute connected to it, hostility. Different computational techniques are used to address this a problem suchlike natural language processing, machine learning and deep learning models. The methodology proposed in this paper forms part of and enhances a previously reported holistic social media analysis framework for national security. The robustness and effectiveness of our approach are tested on one real-world event related to disruptive activity, protests in Puerto Rico in 2019.