# Making Sense of Language Signals for Monitoring Radicalization
## 4 Methods
### Linguistic Processing
It measures five parameters: affiliation, achievement, power, reward, and risk. Feelings such as anxiety, anger, sadness, or positive and negative emotions are part of the \"Affective Process\" set. The \"Personal Concerns\" set measures how worried the person behind the text is about different life issues, such as work, leisure, home, money, religion, and death. Drives, affective concerns, and personal concerns are considered the three main psychological variables and have been used as a reference to analyze radical discourses before [57]. The \"Social Process\" set was also included to provide some more information about references to family, friends, and and female and male authority figures. The LIWC dictionary is composed of 2290 words and word stems [9]. Each word or word-stem defines one or more word category or subdictionary. The LIWC categories are arranged hierarchically. For example, by definition, all anger words will be categorized as negative emotion and overall emotion words. The obtained result enriches and contextualizes the input textual data, offering a detailed analysis covering emotions, thinking styles, social concerns, and personal drivers. Following, the Moral Foundations Theory (MFT) [24] was modeled and included in the intelligent engine, allowing users to study such a complex phenomenon automatically. The MFT expresses the psychological basis of morality in terms of innate intuitions, defining the following five foundations [60; 87]. Although recent, MFT is the most established theory in psychology and the social sciences [88]. It is also widely adopted in computational social science. It defines a clear taxonomy of values and a dictionary of terms, the Moral Foundations Dictionary (MFD) [24], an essential resource for NLP applications. In this task, we adopted the use of the MFD, analyzing the text messages and extracting their expressed moral values. The MFD is used through the LIWC tool, which facilitates text processing. In this way, we process the moral value annotations similarly as described above. The MFD was developed to operationalize moral values in the text. This dictionary provides information on the proportions of virtue and vice words for each moral foundation on a corpus of text [24]. It comprises 336 words and word stems categorized in the five moral dimensions. All moral dimensions were extracted from text, and their results were aggregated to compose a unified view of the data. For this paper, the intelligent engine aggregated the scores obtained for all the relevant categories and presented the results according to the corresponding ideology and narrative. The message's scores were normalized using the total number of words. In order to aggregate the multiple messages collected, the arithmetic mean was calculated for all the LIWC values of the same category for every ideology and narrative. For a given message, the final score was computed using the following expression:  
\[Score=\frac{1}{N_{w}}\sum_{C_{i}\in C}s_{\text{words}\in C_{i}} \tag{1}\]  
with \(C\) being the set of possible categories, \(C_{i}\) an element of \(C\), \(N_{w}\) the number of words in the message, and \(s_{\text{words}\in C_{i}}\) the score given to the words that belong to \(C_{i}\).