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Analysing Social Media as a Hybrid Tool to Detect and Interpret likely Radical Behavioural Traits for National Security

Lookup NU author(s): Professor Boguslaw ObaraORCiD


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© 2019 IEEE. The study of National Security and its associated considerations is a sensitive and complex paradigm. It encapsulates both the protection of the territorial integrity and sovereignty of a state, as well as guaranteeing the security of its population. Known as Human Security, human-centred threats arising from radical activities need to be mitigated else they may escalate and have implications on National Security. The modern era has introduced further disruptive challenges, known as Hybrid Threats, that use non-traditional tools (Hybrid Tools) to intensify the impact of a likely threat. Social Media is a clear illustration of such tools, where the stability of the state and its people can be compromised by the dissemination of material. The ability to identify behaviour bordering on criminality within the deregulated world of Social Media is a Human Security imperative for governments. This paper follows on from our earlier work to detect affected National Security variables through the analysis of social media communication and trigger an alert when a likely threat is detected. As a result, a set of crisis interpretation processes are started to construe the event, such as radical behaviour analysis.This paper details the methodological approach to analyse one Hybrid Tool (Social Media) in order to identify likely instability scenarios based on the Human Security spectrum and therefore extract, detect and interpret dissimilar behavioural patterns that outline radical behavioural traits for National Security. The proposed methodology focuses on five steps, namely Instability Scenarios, Entity Extraction, Wordlists Creation, Content Analytics, and Data Interpretation.

Publication metadata

Author(s): Cardenas P, Obara B, Theodoropoulos G, Kureshi I

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE International Conference on Big Data (Big Data 2019)

Year of Conference: 2019

Pages: 4579-4588

Online publication date: 24/02/2020

Acceptance date: 02/04/2018

Publisher: IEEE


DOI: 10.1109/BigData47090.2019.9006259

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728108582