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 proposes to complement social science approaches with computational methods to detect these factors automatically by analyzing the language signals expressed in social networks. To this end, the article categorizes radicalization drivers and factors following the micro, meso, and macro levels used in the social sciences. It identifies the corresponding language signals and available language resources. Then, a computational system is developed to monitor these language signals. In addition, this article proposes semantic technologies since they offer unique exploration, query, and discovery capabilities. The system was evaluated based on a set of competency questions that show the benefits of this approach.

The purpose of this document is to make recommendations on the necessity to transform current P/CVE policies, emphasizing the importance of a more effective involvement of Politics to contrast the spread of extremism and radicalisation in Europe. In fact, in a syndemic and post-Caliphate world, problems and challenges toward political violence are more and more institutional, systemic, and political. According to the first HORIZON2020 PARTICIPATION PROJECT’s results, the crisis of participation and the crisis of integration are closely linked to current extremism and radicalisation processes; at the same time, fragmentation, interrelation between different kind of extremism and hybridisation, request a new approach that involve, in a more effective way, civil society and political society to contrast extremism, radicalisation and terrorism.