Chaos in Order: Applying ML, NLP, and Chaos Theory in Open Source Intelligence for Counter-Terrorism

Abstract

The present research aims to investigate whether Chaos Theory can be combined with Machine Learning and Natural Language Processing to apply these techniques to Open Source Intelligence (OSINT) analysis. Describing the role of OSINT in different domains and highlighting chaos as a valuable resource for information gathering, the study highlights that the substantial volume, swift velocity, and extensive variety of open-source data pose significant challenges. To address these challenges it is proposed to apply elements of Chaos Theory and advanced computational methods to open-source data. Key concepts from Chaos Theory that will be explored are the ‘Butterfly Effect’, and ‘Strange Attractors’, attempting to demonstrate that chaotic aspects of data can be exploited and transformed into dynamic and powerful sources of information. To support the above, the research includes a case study that exploits and analyses data from Reddit posts and concludes that recognizing and exploiting the dynamic interaction between order and chaos places Chaos Theory not only complementary but as a foundational stone of the overall OSINT toolkit, in the hands of intelligence analysts.