This thesis pursues the objective of leveraging social media data and Artificial Intelligence (AI) techniques to foster secure societies against online and offline threats. Thus, it provides contributions in two directions: (i) uncovering threats in online ecosystems, and (ii) grounding the online information on the unfolding offline threats. In both cases, on the one hand, we investigated how to improve AI techniques that enable essential applications; on the other, we devised and applied comprehensive approaches to target specific threats.
Focusing on uncovering online threats, our first contribution consists of a general deep learning framework for automatically detecting extremist propaganda contents on OSM when approaching realistic conditions. In the second contribution, we mapped and characterized cryptocurrency manipulations within a large online ecosystem, focusing on the mechanisms adopted by scammers to recruit participants. Finally, in the third contribution, we devised a new network-based framework for uncovering and characterizing coordinated behavior on social media in the context of the recent UK General Election.
Moving to the problem of grounding online information on unfolding offline threats, we proposed a new technique for solving the geoparsing problem, which consists of identifying location mentions in text and linking them to the corresponding geographic coordinates. Finally, our last contribution is an AI-powered system for enriching available social crisis data in the aftermath of mass disasters with solicited information from on-the-ground witnesses
