# Making Sense of Language Signals for Monitoring Radicalization
## 6 Conclusions  
This paper presents the work to develop an intelligent engine to detect radicalization in youth. Such efforts revolve around the following objectives: (i) the development of an orchestrated intelligent platform that captures, processes, and stores data from social networks; (ii) the development of several text analysis methods for contextualizing and enriching the captured data; (iii) the generation of several semantic vocabularies that allow the semantic modeling of the captured data and their corresponding analyses; and (iv) the development of a visualization dashboard that allows users to gain insights into the produced data, follow trends in the data, and perform complex queries. The efforts in this work have produced a complete big data system that exploits several linguistic analysis methods and the full potential of semantic modeling, offering users a complete yet straightforward graphical interface for interacting with the data. The mentioned intelligent platform has been developed and deployed for its use in future work. For example, it can monitor recruitment and propaganda campaigns. Interoperability and extensibility are fundamental principles followed during the development phase. Such a design enables third parties to use the data extracted by our tools rapidly. Several efforts have been made concerning modeling the data, its analyses, and visual representations. These novel advances have brought the semantic modeling of a known resource in the NLP context: the LIWC dictionary. Such representations can be used jointly with other annotations, thereby facilitating their replication. In addition, in this task,the MFT concepts have also been modeled using semantic representations. The advances in visualization and querying technologies allow domain experts to quickly detect patterns in the captured data using standard web technologies, facilitating their use. It is of particular relevance to the work done in relation to RQ1 (_What are the main linguistic signals that reveal radicalization factors?_). As detailed in Section 2, a large number of studies on radicalization drivers have been addressed as computational radicalization signals. The proposed system covers a number of these approaches, showing its utility in a real-world scenario. Additionally, this work presented several novel semantic vocabularies used throughout the presented system. More concretely, this paper presented the (i) Senpy Ontology vocabulary (SENPY), (ii) Semantic LIWC (SLIWC) vocabulary, (iii) Morality vocabulary, and (iv) Narrative vocabulary. In this way, our system is able to semantically model radicalization and extremist processes with a focus on language. This work aims at providing a pathway between traditional and computational social science. Traditionally, the primary data sources for radicalization analysis are focus groups and interviews with experts, police forces, or convicted terrorists and extremists. As a result, the sample size is usually small. In contrast, social data analysis allows the analysis of large quantities of messages and interactions. This work can help social scientists to complement their studies in several ways. First, our system can be used for analyzing a specific event in a specific region, which can complement the analysis of specific cases. In addition, an interesting aspect that can be analyzed is the temporal evolution in the radicalization processes. Understanding this evolution can help with designing better counter-radicalization strategies. Finally, automated text analytics can help gain insights into radicalism's drivers and roots. To this end, this work provides complementary text analytics, such as sentiment and emotion analysis, social-psychological analysis, and moral foundations analysis. Social scientists can benefit from these analyses to better understand the processes and interactions of radicalization factors.