Dynamic Detection of Radical Profiles in Social Networks Using Image Feature Descriptors and a Case-Based Reasoning Methodology

Nowadays, security forces are challenged by a new type of terrorist propaganda which occurs in public social networks and targets vulnerable individuals. The current volume of online radicalization messages has rendered manual monitoring approaches unfeasible, and effective countermeasures can only be adopted through early detection by automatized tools. Some approaches focus on mining the information provided by social users in the form of interactions and textual content. However, radical users also tend to exhibit distinctive iconography in their profile images. In this work, we propose the use of local image descriptors over profile images to aid the detection and monitoring of online radicalization processes. In addition, we complement this approach with an interaction-based formula for risk assessment, so candidate profiles can be selected for image-analysis based on their interaction with confirmed radical profiles. These techniques are combined in the context of a Case-Based Reasoning framework which, together with the feedback provided by the end-user, enables a continuous monitoring of the activity of radical users and eases the discovery of new profiles with a radicalization agenda.