# Making Sense of Language Signals for Monitoring Radicalization
## 5 Results and Evaluation
### Discussion  
The previous evaluation showed the utility of the presented system and the framework it enables. In this way, we have demonstrated how the semantic modeling combined with  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{_Which is the ideology with a higher percentage of polarized content?_} &?ideology &?ratio \\ \hline SELECT?ideology (AVG(?counts) AS?ratio) & Separatism & 0.5232 \\ WHERE {?post a sio:Post;  sliwc:hasASet [ & Fari-left & 0.4476 \\  sliwc:contains [ & & \\   a narr:Annotation ; & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  
a complete NLP and data processing pipeline allows for discovering complex patterns in relation to radicalization and extremism in social media. It is this semantic modeling that allows users to define ad hoc queries that can adapt to complicated necessities from social science actors.  
Attending to the previous literature, the Hatemeter system [91] stands as a complete system that monitors anti-Muslim content in social networks. Hatemeter made use of an Long Short-Term Memory (LSTM) network to perform text classification in the domain of anti-Muslim content. Additionally, Hatemeter has a network-oriented interface, exploring the relations between monitored users. In comparison, the system presented in this paper is language-oriented, as it explores patterns in the linguistic data. Additionally, our system makes use of popular lexical resources (LIWC, MFT) to analyze language, offering a domain-agnostic view of the language and its use.  
Another relevant system for monitoring social media is Contro L'Odio [35], which monitors and contrasts discrimination and hate speech in Italy. This platform also has several visualizations that allow a user to reduce the complexity of the data. As in our system, Contro L'Odio offers a geographical visualization that relates locations and their own language trends.  
The platform of the project Trivalent [38] was designed for law enforcement agencies to monitor social media in the context of jihadist extremism. While Trivalent's platform monitors news and online magazines, the presented system has a focus on social media. Additionally, our system has complete language analysis that, as described, includes several lexical resources.  
As described in Section 4.1, the proposed intelligent engine has a modular architecture that has been implemented using a micro-services fashion. This, of course, would allow us to extend the original functionalities with additional modules that can be added to the data model through the presented semantic modeling. An interesting example will be to extend the presented system with text classification capabilities, such as radicalization or extremism detection. In this line of work, previous studies have used deep learning models, such as word embeddings [10; 92] and several neural architectures [93; 94]. Recent advances in NLP learning methods have used the transformer architecture [95], which has been applied to hate speech and similar content [96]. These mentioned approaches can be easily added to the intelligent engine due to the presented architecture and its corresponding semantic modeling.