# Making Sense of Language Signals for Monitoring Radicalization
## 2 From Radicalization Drivers to Radicalization Signals
The dictionary defines the following categories: planning, violence, weaponry, help-seeking, hate, frustration, suicide, threat, grievance, fixation, desperation, deadline, murder, relationship, loneliness, surveillance, soldier, honor, impostor, jealousy, god, and paranoia. Most categories are correlated to related LIWC categories, as expected. ExtremeSentiLex [29] is a lexicon to detect extreme sentiments based on SentiWordNet [30] and SentiNet [31]. It has detected 29% and 33% of extreme positive posts in the extremist religious forums Ansar1 [32] and TurnToIslam [33], respectively, in contrast with only 2% and 4% of extreme negative posts. While radicalization signals are adequate for understanding radicalization better and its use in surveillance tasks, data processing is not enough. Visualization techniques [34] allow humans to search for patterns more effectively. In addition, visualization techniques can be an excellent tool for dissemination and awareness. Several works have been particularly relevant for the monitoring of hate speech. Contro l'odio [35] is a web platform for monitoring immigrants' discrimination and hate speech in Italy. It provides the results of linguistic analysis in interactive maps. It has been used in educational courses for high school students and citizens with the aim of the deconstruction of negative stereotypes. HateMeter [36] is a European project for monitoring, analyzing, and fighting anti-Muslim hatred online. It has been used to help Non-governmental organizations (NGOs) and social science research analyze and prevent hate speech use [37]. Regarding the linguistic analysis of radicalism, the project Trivalent H2020 [38] developed a dashboard to monitor social media targeted at Law Enforcement Agencies. In the same way, [PERSON] et al. [39] developed a dashboard to enable social network analysts to understand patterns of behavioral disorders over time. Finally, Table 1 summarizes the review presented in this section, including the level type (i.e., micro, meso, macro), the radicalization drivers and factors, the language signals, the data resources, and the references to the literature related to them. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Drivers \& Factors** & **Signal** & **Data Resource** & **References** \\ \hline Micro & Warning behaviors & Linguistic markers, entity recognition, emotions, hate speech, offensive language & Violent term dictionary, lexical databases (e.g., WordNet), LIWC, HateSonar & Lone wolf [17] \\ \hline Micro & Identity crisis, grievance & Negative emotions & LIWC and MFT & Religious [23], Separatist [23] \\ \hline Micro & Social psychological factors & Expression of linguistic dimensions (e.g., pronouns), personal concerns (e.g., death), cognitive process (e.g., certainty), and affective processes (e.g., anger) & LIWC & Religious [25] \\ \hline Micro & Emotional drivers & Extreme opinions & ExtremeSentiLex & Religious [29] \\ \hline Micro & Frustration, introversion, discrimination, identity & Linguistic markers & - & Religious [27] \\ \hline Micro & Grievance & Linguistic markers & Grievance dictionary & Lone wolf and others [28] \\ \hline Micro & Psychological factors & Psychological indicators, Moral values & LIWC, MFT & Political [26] \\ \hline Meso & Group membership & Use of jargon or vernacular & Daesh vernacular dictionary & Religious [23] \\ \hline Meso & Group dynamics & Social context (sequential accounts, followers, followed) & Twitter & Religious [23] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of literature survey regarding language signals for monitoring radicalization.