Enabling Open Source Intelligence (OSINT) in private social networks

Abstract

Open Source Intelligence (OSINT) has been widely acknowledged as a critical source of valuable and cost efficient intelligence that is derived from publicly available sources. With the rise of prominent social media platforms such as Facebook and Twitter that record and expose a multitude of different datasets, investigators are beginning to look at what social media has to offer the Intelligence Community (IC). Some major obstacles that OSINT analysts often face are privacy and platform restrictions that serve both to protect the privacy of individuals and to protect the economic livelihood of the social media platform. In this work we review existing social networking research to examine how it can be applied to OSINT. As our contribution, we propose a greedy search algorithm for enabling efficient discovery of private friends on social networking sites and evaluate its performance on multiple randomly generated graphs as well as a real-world social network collected by other researchers. In its breadth, this work aims to provide the reader with a broader understanding of OSINT and key concepts in social network analysis.