# Making Sense of Language Signals for Monitoring Radicalization
###### 1 Introduction  
Our society has become more polarized in recent years, with sources of division being political, economic, and religious. Polarization threatens social cohesion and can be the source of radicalization and violent extremism. Thus, many countries and international organizations have developed counter-radicalization programs and strategies for radicalization prevention, deradicalization, and disengagement [1].  
This research work aims to contribute to counter-radicalization efforts. It has been developed within the R&D project PARTICIPATION [2], whose purpose is to prevent extremism, radicalization, and polarization that can lead to violence through more effective social and education policies and interventions. The design of these policies will be based on a participatory approach, where radicalization drivers are taken into account to prevent the radicalization process.  
Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include literature reviews, qualitative interviews, focus groups, and quantitative methods based on surveys. This article studies the perspective of complementing social science approaches with computational methods to detect these factors automatically by analyzing the language patterns expressed in social networks. In particular, we aimed to develop a computational system that helps social scientists understand different types of radicalization. For this purpose, three research questions were defined to guide us through the research process:  
1. What are the main linguistic signals that reveal radicalization factors?
2. How can we computationally operate radicalization factors to monitor radicalization?
3. How can we* How can semantic technology provide fine-grained query capabilities to social scientists to get insights from radicalization processes?  
In this way, the contributions of this paper are threefold. First, we present a detailed study of the process that takes current analysis systems from considering radicalization drivers to radicalization signals. Such a step is of paramount importance for the field, since radicalization drivers (e.g., social, psychological, and emotional factors) need to be modeled appropriately from a computational perspective, thereby obtaining radicalization signals. Second, to enhance the data enrichment, the second main contribution of this work is the generation of four novel semantic vocabularies that allow us and the system to model several radicalization and extremism phenomena from a semantic perspective. Finally, to concretely enhance the radicalization signals studied, the third contribution of this work is an intelligent end-to-end engine that monitors radicalization and extreme language on social media using appropriate Natural Language Processing (NLP) methods in the field of radicalization analysis.  
The remainder of this article is organized as follows. First, in Section 2, we review the literature to understand the linguistic signals used to detect radicalization drivers and factors (RQ1). In Section 3, the linked data concepts and vocabularies employed to model the data semantically in this work are summarized (RQ3). Then, in Section 4, we present the software methods and architecture designed to provide social scientists with a dashboard for monitoring radicalization (RQ2 and RQ3). Section 4 also presents the results of the developed software component and the developed dashboard (RQ2). In Section 5, we present an evaluation of the query capabilities of the proposed semantic technology (RQ3). Section 5 includes a discussion comparing the proposed approach with other similar works. Finally, the conclusions of the research done and new perspectives for research are given in Section 6.