# Making Sense of Language Signals for Monitoring Radicalization
## 4 Methods
### Linguistic Processing  
A thorough linguistic study was performed on the captured data as a relevant part of the developed intelligent engine. In this way, the original data were enriched using a linguistic analysis that contextualized and offered valuable insights. One of the most relevant processes involves using two relevant linguistic frameworks intimately related to radicalization and its presence in text: the (i) Linguistic Inquiry and Word Count (LIWC) [84] \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Religious**} & **Far Right** & **Far Left** & **Separatism** \\ \hline \multirow{8}{*}{Pro} & is & supremacy & socialism & indyref \\  & iraq & invasion & cityworkers & Brevit \\  & islamicastate & Great Replacement & Courage & Donbas \\  & alleycsonisis & defendEurope & antifa & VoteLeave \\  & syria & Qanon & commune & separatism \\  & khilafarestored & Pizzagate & Commune71 & PKK \\  & islam & nazism & ViveLaComune & islamicastate \\  & muslims & incel & Marx & \\  & brotherhood & fascism & Revolution & \\  & & Gamergate & & \\  & & AHD & & \\  & & MAGA & & \\  & & antifeminist & & \\ \hline \multirow{8}{*}{Counter} & eurotopia & diversity & AntiAntifa & Dogs AgainstBrevit \\  & antiferroism & stopHate & antisocialism & nationalidentity \\  & antiterror & antifascist & GoodNightLeffside & Framing \\  & peace & nonazis & & \\  & antiterrorist & FCKNZS & & \\  & againstterrorism & noafd & & \\  & stopterrorim & & & \\  & notinmyname & & & \\ \hline \multirow{2}{*}{Alternative} & notanotherbrobther & hopenothate & WhitePrivilege & StrongerIn \\  & wearethemany & LeaveNoOneBehind & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hashtags are used to capture messages organized by ideology and narrative. resource and the (ii) Moral Foundations Theory (MFT) [24]. This selection of resources followed previous works, as described in Section 2 (see Table 1). Once each tweet was selected and tagged by ideology and narrative based on its hashtags, it was pre-processed and analyzed, thereby enriching its original information, using the semantic models shown in Section 3. Consequently, the geographical location was extracted from all tweets, which could aid in determining the evolution of the studied ideologies by country. Indeed, as mentioned above, the text was thoroughly analyzed by automatic means, which could give insight into the psychological factors of the text, and the motivations and ideologies of the author. For the psychological traits, we assessed the analysis provided by the Linguistic Inquiry and Word Count (LIWC) resource [84]. LIWCwas used in previous words to study the radicalization phenomena. As an exciting example, it was used to analyze political posts on Twitter to find a relation between them and the results of a political election [85]. Additionally, [PERSON] et al. [86] studied the quality of the LIWC annotations for ISIS-related messages, obtaining promising results. The LIWC algorithm performs a word count that elicits emotions, psychological meanings, or social concerns in a text. Thus, the process leverages a dictionary that compiles an exhaustive list of words and their possible inflections, classifying them (e.g., anxiety, anger, family, religion). Consequently, every category present in the text is given a score corresponding to the number of words found that belong to that category. As an example, if a sentence contains the word \"father,\" which is classified by the dictionary as \"family\" and \"male reference,\" then both of these categories will have a resulting score of one. Alternatively, if the word \"cousin,\" which is also a reference to \"family,\" also appears in the text, then the \"family\" category would obtain a score of two. The categories measured by LIWC are grouped in sets depending on the psychological process they are describing. Four of them are taken into account for this analysis: drives, affective, personal concerns, and social. The \"Drives\" set gives insight into the motivations of the person behind the text.